diff --git a/.github/workflows/github-actions.yml b/.github/workflows/github-actions.yml
new file mode 100644
index 000000000..d35c3bcdb
--- /dev/null
+++ b/.github/workflows/github-actions.yml
@@ -0,0 +1,56 @@
+on: push
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    services:
+      postgres:
+        image: postgres:latest
+        env:
+          POSTGRES_USER: postgres
+          POSTGRES_PASSWORD: postgres
+          POSTGRES_DB: github_actions
+        ports:
+          - 5432:5432
+        # needed because the postgres container does not provide a healthcheck
+        options: --health-cmd pg_isready --health-interval 10s --health-timeout 5s --health-retries 5
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.8
+    - name: python ldap
+      run: sudo apt-get install libsasl2-dev python3-dev libldap2-dev libssl-dev #adding package for python ldap error 
+    - name: Install dependencies
+      run: |
+        pip install --upgrade pip
+        python -m pip install python-ldap
+        pip install -r requirements-dev.txt
+    - name: Run migrations
+      run: python manage.py migrate
+      env:
+          BEAGLE_DB_NAME: github_actions
+          BEAGLE_DB_PASSWORD: postgres
+          BEAGLE_DB_USERNAME: postgres
+    - name: Run test
+      run: |
+        python manage.py test
+      env:
+          BEAGLE_DB_NAME: github_actions
+          BEAGLE_DB_PASSWORD: postgres
+          BEAGLE_DB_USERNAME: postgres
+          BEAGLE_DB_PORT: 5432
+          BEAGLE_NOTIFIER_ACTIVE: False
+          TMPDIR: /tmp
+          BEAGLE_SHARED_TMPDIR: $TMPDIR
+          ENVIRONMENT: 'dev'
+          BEAGLE_PRIMARY_ID_REGEX: "^(0\\d{4}|[1-9]\\d{4,})(|_[B-Z]|_[A-Z]{2,})_\\d+$"
+    - name: Black check
+      uses: psf/black@stable
+      with:
+        options: "--check"
+        version: "22.3.0"
diff --git a/beagle/__init__.py b/beagle/__init__.py
index 9e9fe4198..f91f60510 100644
--- a/beagle/__init__.py
+++ b/beagle/__init__.py
@@ -1 +1 @@
-__version__ = "1.88.0"
+__version__ = "1.91.2"
diff --git a/beagle_etl/fixtures/beagle_etl.operator.json b/beagle_etl/fixtures/beagle_etl.operator.json
index 843caf9e4..01612f363 100644
--- a/beagle_etl/fixtures/beagle_etl.operator.json
+++ b/beagle_etl/fixtures/beagle_etl.operator.json
@@ -251,5 +251,16 @@
       "version": "v1.2.0",
       "slug": "ArgosBamOperator_v1_0_0"
     }
+  },
+  {
+    "model": "beagle_etl.operator",
+    "pk": 25,
+    "fields": {
+      "active": true,
+      "recipes": "[\"None\"]",
+      "class_name": "runner.operator.copy_outputs_operator.v2_1_0.copy_outputs_operator.CopyOutputsOperator",
+      "version": "v2.1.0",
+      "slug": "CopyOutputsOperator_v2.1.0"
+    }
   }
 ]
\ No newline at end of file
diff --git a/file_system/helper/access_helper.py b/file_system/helper/access_helper.py
index 807bc7134..af4220ece 100644
--- a/file_system/helper/access_helper.py
+++ b/file_system/helper/access_helper.py
@@ -81,8 +81,14 @@ def construct_csv(self):
         """
 
         # get fastq metadata for a given request
-        fastqs = File.objects.filter(file_group__slug="lims", request_id__in=self.request_ids)
-        fastq_metadata = [fastq.filemetadata_set.values()[0]["metadata"] for fastq in fastqs]
+        files = FileRepository.filter(
+            file_group=settings.IMPORT_FILE_GROUP,
+            metadata={settings.IGO_COMPLETE_METADATA_KEY: True},
+            filter_redact=True,
+        ).all()
+        fastqs = files.filter(metadata__igoRequestId__in=self.request_ids)
+        fastq_metadata = [fastq.metadata for fastq in fastqs]
+        fastq_metadata = sorted(fastq_metadata, key=lambda d: d["cmoSampleName"])
         cmoPatientId = set([fastq["cmoPatientId"] for fastq in fastq_metadata])
         # get DMP BAM file group
         dmp_bams = FileRepository.filter(file_group=settings.DMP_BAM_FILE_GROUP)
diff --git a/fixtures/tests/08944_B.argos.input.json b/fixtures/tests/08944_B.argos.input.json
new file mode 100644
index 000000000..2ae4f646a
--- /dev/null
+++ b/fixtures/tests/08944_B.argos.input.json
@@ -0,0 +1,740 @@
+[
+  {
+    "tumor": {
+      "ID": "s_C_MP76JR_X001_d",
+      "CN": "MSKCC",
+      "LB": "08944_B_1_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "H3MV7BBXY"
+      ],
+      "R1": [],
+      "R2": [],
+      "zR1": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG002_P3_17096_L1_IGO_08944_B_1/LTG002_P3_17096_L1_IGO_08944_B_1_S82_R1_001.fastq.gz"
+        }
+      ],
+      "zR2": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG002_P3_17096_L1_IGO_08944_B_1/LTG002_P3_17096_L1_IGO_08944_B_1_S82_R2_001.fastq.gz"
+        }
+      ],
+      "bam": [],
+      "RG_ID": [
+        "s_C_MP76JR_X001_d_H3MV7BBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_MP76JR_X001_d.bam",
+      "request_id": "08944_B",
+      "specimen_type": "PDX"
+    },
+    "normal": {
+      "ID": "s_C_MP76JR_N001_d",
+      "CN": "MSKCC",
+      "LB": "08944_B_2_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "H3MV7BBXY"
+      ],
+      "R1": [],
+      "R2": [],
+      "zR1": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0001535a_IGO_08944_B_2/17-0001535a_IGO_08944_B_2_S83_R1_001.fastq.gz"
+        }
+      ],
+      "zR2": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0001535a_IGO_08944_B_2/17-0001535a_IGO_08944_B_2_S83_R2_001.fastq.gz"
+        }
+      ],
+      "bam": [],
+      "RG_ID": [
+        "s_C_MP76JR_N001_d_H3MV7BBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_MP76JR_N001_d.bam",
+      "request_id": "08944_B",
+      "specimen_type": "Blood"
+    },
+    "assay": "IMPACT468_BAITS",
+    "pi": "John Doe",
+    "pi_email": "email@mskcc.org",
+    "patient_id": "C-MP76JR",
+    "curated_bams": [
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006708_N001_d.Group9.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006709_N001_d.Group11.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006710_N001_d.Group5.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006711_N001_d.Group7.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006713_N001_d.Group10.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006714_N001_d.Group8.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006715_N001_d.Group6.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006716_N001_d.Group4.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006717_N001_d.Group3.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006718_N001_d.Group1.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_09TAFV_N001_d.Group20.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_1FPU8J_N001_d.Group10.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_29F7HE_N001_d.Group17.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_2J273K_N001_d.Group22.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_32WX55_N001_d.Group17.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_3WFCFP_N001_d.Group24.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_4XFY8V_N001_d.Group2.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_7XDAJW_N001_d.Group15.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_96X4FW_N001_d.Group13.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_A8N9WX_N001_d.Group12.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_CTR0E9_N001_d.Group25.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_DJE4C0_N001_d.Group8.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_FDR1YC_N001_d.Group27.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_FWK8C0_N001_d.Group8.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_HRE3UJ_N001_d.Group9.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_JFM1K9_N001_d.Group3.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_JWYL4J_N001_d.Group19.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_KAT85C_N001_d.Group15.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_LC80PX_N001_d.Group7.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_LNNV52_N001_d.Group26.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_MWF8PH_N001_d.Group6.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_NJHH88_N001_d.Group16.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_NR3W32_N001_d.Group21.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_P10A8A_N001_d.Group29.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_U53DXH_N001_d.Group1.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_U8A7CR_N001_d.Group14.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_VXC8TK_N001_d.Group24.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_W0TXP0_N001_d.Group22.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_WWTK32_N001_d.Group18.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_XXC7N6_N001_d.Group12.rg.md.abra.printreads.bam"
+      }
+    ],
+    "hapmap": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/hapmap/hapmap_3.3.b37.vcf"
+    },
+    "dbsnp": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/dbsnp/dbsnp_138.b37.excluding_sites_after_129.vcf"
+    },
+    "indels_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf"
+    },
+    "snps_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/snps_1000g/1000G_phase1.snps.high_confidence.b37.vcf"
+    },
+    "cosmic": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/cosmic/CosmicCodingMuts_v67_b37_20131024__NDS.vcf"
+    },
+    "exac_filter": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/vep/cache/ExAC_nonTCGA.r0.3.1.sites.vep.vcf.gz"
+    },
+    "ref_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta"
+    },
+    "mouse_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCm38/GRCm38.fasta"
+    },
+    "db_files": {
+      "refseq": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/refseq/refGene_b37.sorted.txt"
+      },
+      "vep_data": "/var/cache",
+      "hotspot_list": "/usr/bin/ngs-filters/data/hotspot-list-union-v1-v2.txt",
+      "hotspot_list_maf": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/qc_resources/hotspot-list-union-v1-v2.maf"
+      },
+      "delly_exclude": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/delly/human.hg19.excl.tsv"
+      },
+      "hotspot_vcf": "/usr/bin/basicfiltering/data/hotspot-list-union-v1-v2.vcf",
+      "facets_snps": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/facets_snps/dbsnp_137.b37__RmDupsClean__plusPseudo50__DROP_SORT.vcf.gz"
+      },
+      "custom_enst": "/usr/bin/vcf2maf/data/isoform_overrides_at_mskcc",
+      "vep_path": "/usr/bin/vep",
+      "conpair_markers": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.txt",
+      "conpair_markers_bed": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.bed",
+      "bait_intervals": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_baits.interval_list"
+      },
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+        "DEL",
+        "INV",
+        "INS",
+        "BND"
+      ],
+      "facets_cval": 50,
+      "facets_pcval": 100,
+      "complex_nn": 0.2,
+      "complex_tn": 0.5,
+      "scripts_bin": "/usr/bin",
+      "gatk_jar_path": "/usr/bin/gatk.jar",
+      "pi": "John Doe",
+      "pi_email": "email@mskcc.org"
+    }
+  }
+]
\ No newline at end of file
diff --git a/fixtures/tests/08944_B.argos_bam.input.json b/fixtures/tests/08944_B.argos_bam.input.json
new file mode 100644
index 000000000..a820b40c1
--- /dev/null
+++ b/fixtures/tests/08944_B.argos_bam.input.json
@@ -0,0 +1,290 @@
+[
+  {
+    "tumor": {
+      "ID": "s_C_MP76JR_X001_d",
+      "CN": "MSKCC",
+      "LB": "08944_B_1_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "H3MV7BBXY"
+      ],
+      "R1": [],
+      "R2": [],
+      "zR1": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG002_P3_17096_L1_IGO_08944_B_1/LTG002_P3_17096_L1_IGO_08944_B_1_S82_R1_001.fastq.gz"
+        }
+      ],
+      "zR2": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG002_P3_17096_L1_IGO_08944_B_1/LTG002_P3_17096_L1_IGO_08944_B_1_S82_R2_001.fastq.gz"
+        }
+      ],
+      "bam": [],
+      "RG_ID": [
+        "s_C_MP76JR_X001_d_H3MV7BBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_MP76JR_X001_d.bam",
+      "request_id": "08944_B",
+      "specimen_type": "PDX"
+    },
+    "normal": {
+      "ID": "s_C_MP76JR_N001_d",
+      "CN": "MSKCC",
+      "LB": "08944_B_2_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "H3MV7BBXY"
+      ],
+      "R1": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0001535a_IGO_08944_B_2/17-0001535a_IGO_08944_B_2_S83_R1_001.fastq.gz"
+        }
+      ],
+      "R2": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0001535a_IGO_08944_B_2/17-0001535a_IGO_08944_B_2_S83_R2_001.fastq.gz"
+        }
+      ],
+      "zR1": [],
+      "zR2": [],
+      "bam": [],
+      "RG_ID": [
+        "s_C_MP76JR_N001_d_H3MV7BBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_MP76JR_N001_d.bam",
+      "request_id": "08944_B",
+      "specimen_type": "Blood"
+    },
+    "assay": "IMPACT468_BAITS",
+    "pi": "John Doe",
+    "pi_email": "email@mskcc.org",
+    "patient_id": "C-MP76JR",
+    "genome": "GRCh37",
+    "intervals": [
+      "1",
+      "2",
+      "3",
+      "4",
+      "5",
+      "6",
+      "7",
+      "8",
+      "9",
+      "10",
+      "11",
+      "12",
+      "13",
+      "14",
+      "15",
+      "16",
+      "17",
+      "18",
+      "19",
+      "20",
+      "21",
+      "22",
+      "X",
+      "Y",
+      "MT"
+    ],
+    "opt_dup_pix_dist": "2500",
+    "hapmap": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/hapmap/hapmap_3.3.b37.vcf"
+    },
+    "dbsnp": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/dbsnp/dbsnp_138.b37.excluding_sites_after_129.vcf"
+    },
+    "indels_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf"
+    },
+    "snps_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/snps_1000g/1000G_phase1.snps.high_confidence.b37.vcf"
+    },
+    "covariates": [
+      "CycleCovariate",
+      "ContextCovariate",
+      "ReadGroupCovariate",
+      "QualityScoreCovariate"
+    ],
+    "abra_ram_min": 84000,
+    "gatk_jar_path": "/usr/bin/gatk.jar",
+    "conpair_markers_bed": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.bed",
+    "ref_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta"
+    },
+    "mouse_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCm38/GRCm38.fasta"
+    },
+    "bait_intervals": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_baits.interval_list"
+    },
+    "target_intervals": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_targets.interval_list"
+    },
+    "fp_intervals": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/FP_tiling_intervals.list"
+    }
+  },
+  {
+    "tumor": {
+      "ID": "s_C_4LM16H_X001_d",
+      "CN": "MSKCC",
+      "LB": "08944_B_3_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "H3MV7BBXY"
+      ],
+      "R1": [],
+      "R2": [],
+      "zR1": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG017_P1_17095a_R_43_IGO_08944_B_3/LTG017_P1_17095a_R_43_IGO_08944_B_3_S84_R1_001.fastq.gz"
+        }
+      ],
+      "zR2": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG017_P1_17095a_R_43_IGO_08944_B_3/LTG017_P1_17095a_R_43_IGO_08944_B_3_S84_R2_001.fastq.gz"
+        }
+      ],
+      "bam": [],
+      "RG_ID": [
+        "s_C_4LM16H_X001_d_H3MV7BBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_4LM16H_X001_d.bam",
+      "request_id": "08944_B",
+      "specimen_type": "PDX"
+    },
+    "normal": {
+      "ID": "s_C_4LM16H_N001_d",
+      "CN": "MSKCC",
+      "LB": "08944_B_4_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "H3MV7BBXY"
+      ],
+      "R1": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0010557a_IGO_08944_B_4/17-0010557a_IGO_08944_B_4_S85_R1_001.fastq.gz"
+        }
+      ],
+      "R2": [
+        {
+          "class": "File",
+          "location": "juno:///juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0010557a_IGO_08944_B_4/17-0010557a_IGO_08944_B_4_S85_R2_001.fastq.gz"
+        }
+      ],
+      "zR1": [],
+      "zR2": [],
+      "bam": [],
+      "RG_ID": [
+        "s_C_4LM16H_N001_d_H3MV7BBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_4LM16H_N001_d.bam",
+      "request_id": "08944_B",
+      "specimen_type": "Blood"
+    },
+    "assay": "IMPACT468_BAITS",
+    "pi": "John Doe",
+    "pi_email": "email@mskcc.org",
+    "patient_id": "C-4LM16H",
+    "genome": "GRCh37",
+    "intervals": [
+      "1",
+      "2",
+      "3",
+      "4",
+      "5",
+      "6",
+      "7",
+      "8",
+      "9",
+      "10",
+      "11",
+      "12",
+      "13",
+      "14",
+      "15",
+      "16",
+      "17",
+      "18",
+      "19",
+      "20",
+      "21",
+      "22",
+      "X",
+      "Y",
+      "MT"
+    ],
+    "opt_dup_pix_dist": "2500",
+    "hapmap": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/hapmap/hapmap_3.3.b37.vcf"
+    },
+    "dbsnp": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/dbsnp/dbsnp_138.b37.excluding_sites_after_129.vcf"
+    },
+    "indels_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf"
+    },
+    "snps_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/snps_1000g/1000G_phase1.snps.high_confidence.b37.vcf"
+    },
+    "covariates": [
+      "CycleCovariate",
+      "ContextCovariate",
+      "ReadGroupCovariate",
+      "QualityScoreCovariate"
+    ],
+    "abra_ram_min": 84000,
+    "gatk_jar_path": "/usr/bin/gatk.jar",
+    "conpair_markers_bed": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.bed",
+    "ref_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta"
+    },
+    "mouse_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCm38/GRCm38.fasta"
+    },
+    "bait_intervals": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_baits.interval_list"
+    },
+    "target_intervals": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_targets.interval_list"
+    },
+    "fp_intervals": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/FP_tiling_intervals.list"
+    }
+  }
+]
\ No newline at end of file
diff --git a/fixtures/tests/08944_B.fixtures.json b/fixtures/tests/08944_B.fixtures.json
new file mode 100644
index 000000000..7fb2ab7ba
--- /dev/null
+++ b/fixtures/tests/08944_B.fixtures.json
@@ -0,0 +1,954 @@
+[
+  {
+    "model": "file_system.filemetadata",
+    "pk": "f8ad0d7e-65ce-4d5c-8e79-94be84041651",
+    "fields": {
+      "created_date": "2022-12-07T15:05:43.600Z",
+      "modified_date": "2022-12-07T15:05:43.600Z",
+      "file": "ecee9f31-4abe-455d-adaa-907f9643c27c",
+      "version": 0,
+      "latest": true,
+      "metadata": {
+        "R": "R1",
+        "sex": "F",
+        "ciTag": "s_C_MP76JR_N001_d",
+        "runId": "PITT_0319",
+        "tubeId": "",
+        "baitSet": "IMPACT468_BAITS",
+        "piEmail": "email@mskcc.org",
+        "runDate": "2019-02-11",
+        "runMode": "HiSeq High Output",
+        "species": "Human",
+        "platform": "Illumina",
+        "barcodeId": null,
+        "genePanel": "IMPACT468",
+        "primaryId": "08944_B_2",
+        "qcReports": [],
+        "datasource": "igo",
+        "dnaInputNg": null,
+        "flowCellId": "H3MV7BBXY",
+        "importDate": "2022-11-28",
+        "readLength": "",
+        "sampleName": "17-0001535a",
+        "sampleType": "Normal",
+        "captureName": "Pool-08944_B-Tube7_1",
+        "igoComplete": true,
+        "labHeadName": "John Doe",
+        "sampleClass": "Blood",
+        "barcodeIndex": null,
+        "cmoInfoIgoId": null,
+        "cmoPatientId": "C-MP76JR",
+        "igoProjectId": "08944",
+        "igoRequestId": "08944_B",
+        "labHeadEmail": "email@mskcc.org",
+        "libraryIgoId": "08944_B_2_1_1_1",
+        "oncotreeCode": null,
+        "preservation": "Frozen",
+        "sampleOrigin": "Whole Blood",
+        "cmoSampleName": "C-MP76JR-N001-d",
+        "flowCellLanes": [
+          5
+        ],
+        "libraryVolume": 35.0,
+        "sampleAliases": [
+          {
+            "value": "17-0001535a",
+            "namespace": "investigatorId"
+          },
+          {
+            "value": "08944_B_2",
+            "namespace": "igoId"
+          }
+        ],
+        "smileSampleId": "34e815cf-468f-49f3-953a-bccd11c4461b",
+        "tumorOrNormal": "Normal",
+        "captureInputNg": "170.0",
+        "cfDNA2dBarcode": null,
+        "collectionYear": "",
+        "patientAliases": [
+          {
+            "value": "C-MP76JR",
+            "namespace": "cmoId"
+          }
+        ],
+        "qcAccessEmails": ",email@mskcc.org",
+        "smilePatientId": "2455a56f-f198-47db-a592-ab12137ebc17",
+        "tissueLocation": "",
+        "dataAnalystName": "John Doe",
+        "dataAccessEmails": "",
+        "dataAnalystEmail": "email@mskcc.org",
+        "investigatorName": "John Doe",
+        "sequencingCenter": "MSKCC",
+        "cmoSampleIdFields": {
+          "recipe": "IMPACT468",
+          "sampleType": "DNA",
+          "naToExtract": "",
+          "normalizedPatientId": "ALLAJV_HCISCLC002"
+        },
+        "investigatorEmail": "email@mskcc.org",
+        "otherContactEmails": "email@mskcc.org",
+        "projectManagerName": "John Doe",
+        "additionalProperties": {
+          "isCmoSample": "true",
+          "igoRequestId": "08944_B"
+        },
+        "investigatorSampleId": "17-0001535a",
+        "captureConcentrationNm": "5.074626865671642",
+        "libraryConcentrationNgul": 33.5
+      },
+      "user": null
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "7335531b-72b9-4890-a65f-fee4d0959e12",
+    "fields": {
+      "created_date": "2022-12-07T15:05:43.474Z",
+      "modified_date": "2022-12-07T15:05:43.474Z",
+      "file": "f6f653e8-dcfc-481b-b660-f0f393f6d9de",
+      "version": 0,
+      "latest": true,
+      "metadata": {
+        "R": "R1",
+        "sex": "F",
+        "ciTag": "s_C_MP76JR_X001_d",
+        "runId": "PITT_0319",
+        "tubeId": "",
+        "baitSet": "IMPACT468_BAITS",
+        "piEmail": "email@mskcc.org",
+        "runDate": "2019-02-11",
+        "runMode": "HiSeq High Output",
+        "species": "Human",
+        "platform": "Illumina",
+        "barcodeId": null,
+        "genePanel": "IMPACT468",
+        "primaryId": "08944_B_1",
+        "qcReports": [],
+        "datasource": "igo",
+        "dnaInputNg": null,
+        "flowCellId": "H3MV7BBXY",
+        "importDate": "2022-11-28",
+        "readLength": "",
+        "sampleName": "LTG002_P3_17096_L1",
+        "sampleType": "Primary",
+        "captureName": "Pool-08944_B-Tube7_1",
+        "igoComplete": true,
+        "labHeadName": "John Doe",
+        "sampleClass": "PDX",
+        "barcodeIndex": null,
+        "cmoInfoIgoId": null,
+        "cmoPatientId": "C-MP76JR",
+        "igoProjectId": "08944",
+        "igoRequestId": "08944_B",
+        "labHeadEmail": "email@mskcc.org",
+        "libraryIgoId": "08944_B_1_1_1_1",
+        "oncotreeCode": "SCLC",
+        "preservation": "Frozen",
+        "sampleOrigin": "Tissue",
+        "cmoSampleName": "C-MP76JR-X001-d",
+        "flowCellLanes": [
+          5
+        ],
+        "libraryVolume": 35.0,
+        "sampleAliases": [
+          {
+            "value": "LTG002_P3_17096_L1",
+            "namespace": "investigatorId"
+          },
+          {
+            "value": "08944_B_1",
+            "namespace": "igoId"
+          }
+        ],
+        "smileSampleId": "9020df2c-76b0-4e79-8fe8-a1da0dfec629",
+        "tumorOrNormal": "Tumor",
+        "captureInputNg": "400.0",
+        "cfDNA2dBarcode": null,
+        "collectionYear": "",
+        "patientAliases": [
+          {
+            "value": "C-MP76JR",
+            "namespace": "cmoId"
+          }
+        ],
+        "qcAccessEmails": ",email@mskcc.org",
+        "smilePatientId": "2455a56f-f198-47db-a592-ab12137ebc17",
+        "tissueLocation": "",
+        "dataAnalystName": "John Doe",
+        "dataAccessEmails": "",
+        "dataAnalystEmail": "email@mskcc.org",
+        "investigatorName": "John Doe",
+        "sequencingCenter": "MSKCC",
+        "cmoSampleIdFields": {
+          "recipe": "IMPACT468",
+          "sampleType": "DNA",
+          "naToExtract": "",
+          "normalizedPatientId": "ALLAJV_HCISCLC002"
+        },
+        "investigatorEmail": "email@mskcc.org",
+        "otherContactEmails": "email@mskcc.org",
+        "projectManagerName": "John Doe",
+        "additionalProperties": {
+          "isCmoSample": "true",
+          "igoRequestId": "08944_B"
+        },
+        "investigatorSampleId": "LTG002_P3_17096_L1",
+        "captureConcentrationNm": "11.49425287356322",
+        "libraryConcentrationNgul": 34.8
+      },
+      "user": null
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "2396036c-7ea8-4643-b112-fff7ac698a9b",
+    "fields": {
+      "created_date": "2022-12-07T15:05:43.500Z",
+      "modified_date": "2022-12-07T15:05:43.500Z",
+      "file": "13d96750-2102-4097-b9ae-e7bb5c6776c4",
+      "version": 0,
+      "latest": true,
+      "metadata": {
+        "R": "R2",
+        "sex": "F",
+        "ciTag": "s_C_MP76JR_X001_d",
+        "runId": "PITT_0319",
+        "tubeId": "",
+        "baitSet": "IMPACT468_BAITS",
+        "piEmail": "email@mskcc.org",
+        "runDate": "2019-02-11",
+        "runMode": "HiSeq High Output",
+        "species": "Human",
+        "platform": "Illumina",
+        "barcodeId": null,
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+    "fields": {
+      "created_date": "2021-03-15T16:42:54.048Z",
+      "modified_date": "2024-06-12T17:04:28.131Z",
+      "file_name": "LTG002_P3_17096_L1_IGO_08944_B_1_S82_R1_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG002_P3_17096_L1_IGO_08944_B_1/LTG002_P3_17096_L1_IGO_08944_B_1_S82_R1_001.fastq.gz",
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+      "size": 933098209,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$828da3499a01e62c844a2d9d916e2990baca8d81",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_1\"]",
+      "patient_id": "C-MP76JR",
+      "available": true
+    }
+  },
+  {
+    "model": "file_system.file",
+    "pk": "13d96750-2102-4097-b9ae-e7bb5c6776c4",
+    "fields": {
+      "created_date": "2021-03-15T16:42:55.529Z",
+      "modified_date": "2024-06-12T17:04:28.217Z",
+      "file_name": "LTG002_P3_17096_L1_IGO_08944_B_1_S82_R2_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG002_P3_17096_L1_IGO_08944_B_1/LTG002_P3_17096_L1_IGO_08944_B_1_S82_R2_001.fastq.gz",
+      "file_type": 1,
+      "size": 1063798887,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$29780618e4107c54aea4a05361ded4dd35f3a34f",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_1\"]",
+      "patient_id": "C-MP76JR",
+      "available": true
+    }
+  },
+  {
+    "model": "file_system.file",
+    "pk": "9ced56e0-3e9a-4199-af6a-746a1137f000",
+    "fields": {
+      "created_date": "2021-03-15T16:42:53.209Z",
+      "modified_date": "2024-06-12T17:04:28.231Z",
+      "file_name": "17-0010557a_IGO_08944_B_4_S85_R1_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0010557a_IGO_08944_B_4/17-0010557a_IGO_08944_B_4_S85_R1_001.fastq.gz",
+      "file_type": 1,
+      "size": 447094784,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$70dd62c2e3439bace690a965dce18bb99e0b82ff",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_4\"]",
+      "patient_id": "C-4LM16H",
+      "available": true
+    }
+  },
+  {
+    "model": "file_system.file",
+    "pk": "ca6f2966-97fe-4592-ad26-902e4c5af2ef",
+    "fields": {
+      "created_date": "2021-03-15T16:42:54.252Z",
+      "modified_date": "2024-06-12T17:04:28.248Z",
+      "file_name": "17-0010557a_IGO_08944_B_4_S85_R2_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0010557a_IGO_08944_B_4/17-0010557a_IGO_08944_B_4_S85_R2_001.fastq.gz",
+      "file_type": 1,
+      "size": 508780796,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$3c6c251afae505cecaffe29205a5d75cae45fd04",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_4\"]",
+      "patient_id": "C-4LM16H",
+      "available": true
+    }
+  },
+  {
+    "model": "file_system.file",
+    "pk": "ad8450cf-b95b-453d-a9d9-0fb854f2c1c4",
+    "fields": {
+      "created_date": "2021-03-15T16:42:53.224Z",
+      "modified_date": "2024-06-12T17:04:28.264Z",
+      "file_name": "17-0001535a_IGO_08944_B_2_S83_R2_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_17-0001535a_IGO_08944_B_2/17-0001535a_IGO_08944_B_2_S83_R2_001.fastq.gz",
+      "file_type": 1,
+      "size": 434118132,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$cea4a479dbbe8cb5bf7c304b7871fb8a25057019",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_2\"]",
+      "patient_id": "C-MP76JR",
+      "available": true
+    }
+  },
+  {
+    "model": "file_system.file",
+    "pk": "76658104-3f7e-41e3-9408-0d9075d9036f",
+    "fields": {
+      "created_date": "2021-03-15T16:42:54.065Z",
+      "modified_date": "2024-06-12T17:04:28.187Z",
+      "file_name": "LTG017_P1_17095a_R_43_IGO_08944_B_3_S84_R1_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG017_P1_17095a_R_43_IGO_08944_B_3/LTG017_P1_17095a_R_43_IGO_08944_B_3_S84_R1_001.fastq.gz",
+      "file_type": 1,
+      "size": 952360556,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$e3b7d1ef35084d4f6e8e9e096d15ccfa33956f0e",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_3\"]",
+      "patient_id": "C-4LM16H",
+      "available": true
+    }
+  },
+  {
+    "model": "file_system.file",
+    "pk": "d3512fa1-847a-41b3-81d2-04ff57eec734",
+    "fields": {
+      "created_date": "2021-03-15T16:42:55.535Z",
+      "modified_date": "2024-06-12T17:04:28.204Z",
+      "file_name": "LTG017_P1_17095a_R_43_IGO_08944_B_3_S84_R2_001.fastq.gz",
+      "path": "/juno/archive/msk/cmo/FASTQ/PITT_0319_BH3MV7BBXY/Project_08944_B/Sample_LTG017_P1_17095a_R_43_IGO_08944_B_3/LTG017_P1_17095a_R_43_IGO_08944_B_3_S84_R2_001.fastq.gz",
+      "file_type": 1,
+      "size": 1082701694,
+      "file_group": "1a1b29cf-3bc2-4f6c-b376-d4c5d701166a",
+      "checksum": "sha1$3aba8adb1b77fd1b76533584588a67dcff63d6dd",
+      "request_id": "08944_B",
+      "sample": null,
+      "samples": "[\"08944_B_3\"]",
+      "patient_id": "C-4LM16H",
+      "available": true
+    }
+  }
+]
\ No newline at end of file
diff --git a/fixtures/tests/10075_D_single_TN_pair.argos.input.json b/fixtures/tests/10075_D_single_TN_pair.argos.input.json
index a437612e7..09f13e5fd 100644
--- a/fixtures/tests/10075_D_single_TN_pair.argos.input.json
+++ b/fixtures/tests/10075_D_single_TN_pair.argos.input.json
@@ -34,7 +34,7 @@
                 "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
                 "bwa_output": "s_C_8VK0V7_R001_d.bam",
                 "request_id": "10075_D",
-                "specimen_type": "Local Recurrence"
+                "specimen_type": "Resection"
             },
             {
                 "ID": "s_C_8VK0V7_N001_d",
diff --git a/fixtures/tests/10075_D_single_TN_pair.argos_1_7_0.input.json b/fixtures/tests/10075_D_single_TN_pair.argos_1_7_0.input.json
new file mode 100644
index 000000000..073ab5333
--- /dev/null
+++ b/fixtures/tests/10075_D_single_TN_pair.argos_1_7_0.input.json
@@ -0,0 +1,371 @@
+[
+  {
+    "tumor": {
+      "ID": "s_C_8VK0V7_R001_d",
+      "CN": "MSKCC",
+      "LB": "10075_D_5_1_1_1",
+      "PL": "Illumina",
+      "PU": [
+        "HFTCNBBXY_GTATTGGC-TTGTCGGT"
+      ],
+      "R1": [
+        {
+          "class": "File",
+          "location": "juno:///ifs/archive/GCL/hiseq/FASTQ/PITT_0439_BHFTCNBBXY/Project_10075_D/Sample_SK_MEL_1091A_T_IGO_10075_D_5/SK_MEL_1091A_T_IGO_10075_D_5_S80_R1_001.fastq.gz"
+        }
+      ],
+      "R2": [
+        {
+          "class": "File",
+          "location": "juno:///ifs/archive/GCL/hiseq/FASTQ/PITT_0439_BHFTCNBBXY/Project_10075_D/Sample_SK_MEL_1091A_T_IGO_10075_D_5/SK_MEL_1091A_T_IGO_10075_D_5_S80_R2_001.fastq.gz"
+        }
+      ],
+      "zR1": [],
+      "zR2": [],
+      "bam": [],
+      "RG_ID": [
+        "s_C_8VK0V7_R001_d_HFTCNBBXY_GTATTGGC-TTGTCGGT"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_8VK0V7_R001_d.bam",
+      "request_id": "10075_D",
+      "specimen_type": "Resection"
+    },
+    "normal": {
+      "ID": "s_C_8VK0V7_N001_d",
+      "CN": "MSKCC",
+      "LB": "10075_D_3",
+      "PL": "Illumina",
+      "PU": [
+        "HCYYWBBXY"
+      ],
+      "R1": [
+        {
+          "class": "File",
+          "location": "juno:///ifs/archive/GCL/hiseq/FASTQ/JAX_0397_BHCYYWBBXY/Project_10075_D/Sample_JW_MEL_007_NORM_IGO_10075_D_3/JW_MEL_007_NORM_IGO_10075_D_3_S15_R1_001.fastq.gz"
+        }
+      ],
+      "R2": [
+        {
+          "class": "File",
+          "location": "juno:///ifs/archive/GCL/hiseq/FASTQ/JAX_0397_BHCYYWBBXY/Project_10075_D/Sample_JW_MEL_007_NORM_IGO_10075_D_3/JW_MEL_007_NORM_IGO_10075_D_3_S15_R2_001.fastq.gz"
+        }
+      ],
+      "zR1": [],
+      "zR2": [],
+      "bam": [],
+      "RG_ID": [
+        "s_C_8VK0V7_N001_d_HCYYWBBXY"
+      ],
+      "adapter": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG",
+      "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
+      "bwa_output": "s_C_8VK0V7_N001_d.bam",
+      "request_id": "10075_D",
+      "specimen_type": "Normal"
+    },
+    "assay": "IMPACT468_BAITS",
+    "pi": "John Smith",
+    "pi_email": "email@internet.com",
+    "patient_id": "C-8VK0V7",
+    "curated_bams": [
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006708_N001_d.Group9.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006709_N001_d.Group11.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006710_N001_d.Group5.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006711_N001_d.Group7.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006713_N001_d.Group10.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006714_N001_d.Group8.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006715_N001_d.Group6.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006716_N001_d.Group4.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006717_N001_d.Group3.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006718_N001_d.Group1.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_09TAFV_N001_d.Group20.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_1FPU8J_N001_d.Group10.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_29F7HE_N001_d.Group17.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_2J273K_N001_d.Group22.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_32WX55_N001_d.Group17.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_3WFCFP_N001_d.Group24.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_4XFY8V_N001_d.Group2.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_7XDAJW_N001_d.Group15.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_96X4FW_N001_d.Group13.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_A8N9WX_N001_d.Group12.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_CTR0E9_N001_d.Group25.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_DJE4C0_N001_d.Group8.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_FDR1YC_N001_d.Group27.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_FWK8C0_N001_d.Group8.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_HRE3UJ_N001_d.Group9.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_JFM1K9_N001_d.Group3.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_JWYL4J_N001_d.Group19.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_KAT85C_N001_d.Group15.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_LC80PX_N001_d.Group7.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_LNNV52_N001_d.Group26.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_MWF8PH_N001_d.Group6.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_NJHH88_N001_d.Group16.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_NR3W32_N001_d.Group21.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_P10A8A_N001_d.Group29.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_U53DXH_N001_d.Group1.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_U8A7CR_N001_d.Group14.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_VXC8TK_N001_d.Group24.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_W0TXP0_N001_d.Group22.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_WWTK32_N001_d.Group18.rg.md.abra.printreads.bam"
+      },
+      {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_XXC7N6_N001_d.Group12.rg.md.abra.printreads.bam"
+      }
+    ],
+    "hapmap": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/hapmap/hapmap_3.3.b37.vcf"
+    },
+    "dbsnp": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/dbsnp/dbsnp_138.b37.excluding_sites_after_129.vcf"
+    },
+    "indels_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf"
+    },
+    "snps_1000g": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/snps_1000g/1000G_phase1.snps.high_confidence.b37.vcf"
+    },
+    "cosmic": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/cosmic/CosmicCodingMuts_v67_b37_20131024__NDS.vcf"
+    },
+    "exac_filter": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/vep/cache/ExAC_nonTCGA.r0.3.1.sites.vep.vcf.gz"
+    },
+    "ref_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta"
+    },
+    "mouse_fasta": {
+      "class": "File",
+      "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCm38/GRCm38.fasta"
+    },
+    "db_files": {
+      "refseq": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/refseq/refGene_b37.sorted.txt"
+      },
+      "vep_data": "/var/cache",
+      "hotspot_list": "/usr/bin/ngs-filters/data/hotspot-list-union-v1-v2.txt",
+      "hotspot_list_maf": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/qc_resources/hotspot-list-union-v1-v2.maf"
+      },
+      "delly_exclude": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/delly/human.hg19.excl.tsv"
+      },
+      "hotspot_vcf": "/usr/bin/basicfiltering/data/hotspot-list-union-v1-v2.vcf",
+      "facets_snps": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/facets_snps/dbsnp_137.b37__RmDupsClean__plusPseudo50__DROP_SORT.vcf.gz"
+      },
+      "custom_enst": "/usr/bin/vcf2maf/data/isoform_overrides_at_mskcc",
+      "vep_path": "/usr/bin/vep",
+      "conpair_markers": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.txt",
+      "conpair_markers_bed": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.bed",
+      "bait_intervals": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_baits.interval_list"
+      },
+      "target_intervals": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_targets.interval_list"
+      },
+      "fp_intervals": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/FP_tiling_intervals.list"
+      },
+      "fp_genotypes": {
+        "class": "File",
+        "location": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/FP_tiling_genotypes.txt"
+      }
+    },
+    "runparams": {
+      "abra_scratch": "/scratch",
+      "abra_ram_min": 84000,
+      "genome": "GRCh37",
+      "intervals": [
+        "1",
+        "2",
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+        "10",
+        "11",
+        "12",
+        "13",
+        "14",
+        "15",
+        "16",
+        "17",
+        "18",
+        "19",
+        "20",
+        "21",
+        "22",
+        "X",
+        "Y",
+        "MT"
+      ],
+      "mutect_dcov": 50000,
+      "mutect_rf": [
+        "BadCigar"
+      ],
+      "num_cpu_threads_per_data_thread": 6,
+      "covariates": [
+        "CycleCovariate",
+        "ContextCovariate",
+        "ReadGroupCovariate",
+        "QualityScoreCovariate"
+      ],
+      "emit_original_quals": true,
+      "num_threads": 10,
+      "assay": "IMPACT468_BAITS",
+      "tmp_dir": "/scratch",
+      "project_prefix": "10075_D",
+      "opt_dup_pix_dist": "2500",
+      "delly_type": [
+        "DUP",
+        "DEL",
+        "INV",
+        "INS",
+        "BND"
+      ],
+      "facets_cval": 50,
+      "facets_pcval": 100,
+      "complex_nn": 0.2,
+      "complex_tn": 0.5,
+      "scripts_bin": "/usr/bin",
+      "gatk_jar_path": "/usr/bin/gatk.jar",
+      "pi": "John Smith",
+      "pi_email": "email@internet.com"
+    }
+  }
+]
\ No newline at end of file
diff --git a/fixtures/tests/10075_D_single_TN_pair.argos_bam.input.json b/fixtures/tests/10075_D_single_TN_pair.argos_bam.input.json
index 402439e8c..b9099404b 100644
--- a/fixtures/tests/10075_D_single_TN_pair.argos_bam.input.json
+++ b/fixtures/tests/10075_D_single_TN_pair.argos_bam.input.json
@@ -108,7 +108,7 @@
             "adapter2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT",
             "bwa_output": "s_C_8VK0V7_R001_d.bam",
             "request_id": "10075_D",
-            "specimen_type": "Local Recurrence"
+            "specimen_type": "Resection"
         },
         "normal": {
             "ID": "s_C_8VK0V7_N001_d",
diff --git a/fixtures/tests/10075_D_single_TN_pair.filemetadata.json b/fixtures/tests/10075_D_single_TN_pair.filemetadata.json
index bbed8c87b..b48707a65 100644
--- a/fixtures/tests/10075_D_single_TN_pair.filemetadata.json
+++ b/fixtures/tests/10075_D_single_TN_pair.filemetadata.json
@@ -56,7 +56,7 @@
         ],
         "tumorOrNormal": "Tumor",
         "captureInputNg": "110.0",
-        "sampleClass": "Local Recurrence",
+        "sampleClass": "Resection",
         "collectionYear": "",
         "tissueLocation": "",
         "dataAnalystName": "",
@@ -130,7 +130,7 @@
         ],
         "tumorOrNormal": "Tumor",
         "captureInputNg": "110.0",
-        "sampleClass": "Local Recurrence",
+        "sampleClass": "Resection",
         "collectionYear": "",
         "tissueLocation": "",
         "dataAnalystName": "",
diff --git a/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.json b/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.json
index 50d019066..41a0b9233 100644
--- a/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.json
+++ b/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.json
@@ -5161,6 +5161,7 @@
         "flowCellId": "H7HCTBBXY",
         "readLength": "101/8/101",
         "sampleName": "C-K2902H-P001-d",
+        "sampleType": "Primary",
         "captureName": null,
         "igoComplete": true,
         "labHeadName": "John Smith",
@@ -5227,6 +5228,7 @@
         "flowCellId": "H7HCTBBXY",
         "readLength": "101/8/101",
         "sampleName": "C-K2902H-P001-d",
+        "sampleType": "Primary",
         "captureName": null,
         "igoComplete": true,
         "labHeadName": "John Smith",
@@ -5293,6 +5295,7 @@
         "flowCellId": "H7FKJBBXY",
         "readLength": "101/8/101",
         "sampleName": "C-K2902H-N001-d",
+        "sampleType": "Primary",
         "captureName": null,
         "igoComplete": true,
         "labHeadName": "John Smith",
@@ -5357,6 +5360,7 @@
         "flowCellId": "H7FKJBBXY",
         "readLength": "101/8/101",
         "sampleName": "C-K2902H-N001-d",
+        "sampleType": "Primary",
         "captureName": null,
         "igoComplete": true,
         "labHeadName": "John Smith",
diff --git a/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.with_disambiguate.json b/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.with_disambiguate.json
new file mode 100644
index 000000000..1894877b2
--- /dev/null
+++ b/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.with_disambiguate.json
@@ -0,0 +1,7212 @@
+[
+  {
+    "model": "runner.run",
+    "pk": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+    "fields": {
+      "created_date": "2019-12-11T22:53:02.012Z",
+      "modified_date": "2020-01-14T23:03:35.530Z",
+      "name": "ARGOS 09670_D, 22 of 54 (12/11/2019, 22:53:02)",
+      "app": "cb5d793b-e650-4b7d-bfcd-882858e29cc5",
+      "status": 4,
+      "execution_id": "df43cc77-bed4-4233-9817-6d12e25d9e1c",
+      "job_statuses": {},
+      "output_metadata": {
+        "assay": "IDT_Exome_v1_FP_b37"
+      },
+      "tags": {
+        "igoRequestId": "09670_D"
+      },
+      "notify_for_outputs": "[\"qc_pdf\"]"
+    }
+  },
+  {
+    "model": "runner.port",
+    "pk": "6a3f41e3-e6f1-4d6f-a877-35c2247b82cb",
+    "fields": {
+      "created_date": "2019-12-11T22:53:17.822Z",
+      "modified_date": "2020-02-04T15:29:30.720Z",
+      "run": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+      "name": "dbsnp",
+      "port_type": 0,
+      "schema": {
+        "type": [
+          "null",
+          "array"
+        ],
+        "items": "File"
+      },
+      "secondary_files": [
+        ".idx"
+      ],
+      "db_value": {
+        "class": "File",
+        "location": "bid://914dce04-bc41-427e-9b6c-3084bfa61250"
+      },
+      "value": {
+        "path": "/juno/work/ci/resources/request_files/dbsnp/dbsnp_138.b37.excluding_sites_after_129.vcf",
+        "size": 2432705678,
+        "class": "File"
+      },
+      "files": [
+        "914dce04-bc41-427e-9b6c-3084bfa61250"
+      ]
+    }
+  },
+  {
+    "model": "runner.port",
+    "pk": "704ee521-9779-4754-b270-ba8216f09c34",
+    "fields": {
+      "created_date": "2019-12-11T22:53:17.827Z",
+      "modified_date": "2020-02-04T15:29:30.724Z",
+      "run": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+      "name": "indels_1000g",
+      "port_type": 0,
+      "schema": {
+        "type": [
+          "null",
+          "array"
+        ],
+        "items": "File"
+      },
+      "secondary_files": [
+        ".idx"
+      ],
+      "db_value": {
+        "class": "File",
+        "location": "bid://c868b6e9-92bd-4855-9726-7945e4471973"
+      },
+      "value": {
+        "path": "/juno/work/ci/resources/request_files/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf",
+        "size": 86369975,
+        "class": "File"
+      },
+      "files": [
+        "c868b6e9-92bd-4855-9726-7945e4471973"
+      ]
+    }
+  },
+  {
+    "model": "runner.port",
+    "pk": "52e17010-65a2-4e7d-a699-03bfa8c7f62e",
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+      "modified_date": "2020-02-04T15:29:30.750Z",
+      "run": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+      "name": "snps_1000g",
+      "port_type": 0,
+      "schema": {
+        "type": [
+          "null",
+          "array"
+        ],
+        "items": "File"
+      },
+      "secondary_files": [
+        ".idx"
+      ],
+      "db_value": {
+        "class": "File",
+        "location": "bid://6f644f7f-a7b7-4573-a40a-6c5838f58b75"
+      },
+      "value": {
+        "path": "/juno/work/ci/resources/request_files/snps_1000g/1000G_phase1.snps.high_confidence.b37.vcf",
+        "size": 7313069069,
+        "class": "File"
+      },
+      "files": [
+        "6f644f7f-a7b7-4573-a40a-6c5838f58b75"
+      ]
+    }
+  },
+  {
+    "model": "runner.port",
+    "pk": "b51ad3f0-e81f-45d6-a7ea-915939644abd",
+    "fields": {
+      "created_date": "2019-12-11T22:53:17.836Z",
+      "modified_date": "2020-02-04T15:29:30.924Z",
+      "run": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+      "name": "cosmic",
+      "port_type": 0,
+      "schema": {
+        "type": [
+          "null",
+          "array"
+        ],
+        "items": "File"
+      },
+      "secondary_files": [
+        ".idx"
+      ],
+      "db_value": {
+        "class": "File",
+        "location": "bid://100204c9-c560-43d9-ab4d-8c03bc207f4e"
+      },
+      "value": {
+        "path": "/juno/work/ci/resources/request_files/cosmic/CosmicCodingMuts_v67_b37_20131024__NDS.vcf",
+        "size": 112402812,
+        "class": "File"
+      },
+      "files": [
+        "100204c9-c560-43d9-ab4d-8c03bc207f4e"
+      ]
+    }
+  },
+  {
+    "model": "runner.port",
+    "pk": "e656d671-1873-4268-8f62-0e786496ff19",
+    "fields": {
+      "created_date": "2019-12-11T22:53:17.843Z",
+      "modified_date": "2020-02-04T15:29:31.049Z",
+      "run": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+      "name": "exac_filter",
+      "port_type": 0,
+      "schema": {
+        "type": [
+          "null",
+          "array"
+        ],
+        "items": "File"
+      },
+      "secondary_files": [
+        ".tbi"
+      ],
+      "db_value": {
+        "class": "File",
+        "location": "bid://5c1dfb8e-d831-43f1-892b-f3c0bf043ca2"
+      },
+      "value": {
+        "path": "/juno/work/ci/resources/vep/cache/ExAC_nonTCGA.r0.3.1.sites.vep.vcf.gz",
+        "size": 337197976,
+        "class": "File"
+      },
+      "files": [
+        "5c1dfb8e-d831-43f1-892b-f3c0bf043ca2"
+      ]
+    }
+  },
+  {
+    "model": "runner.port",
+    "pk": "fa222a47-ba06-4e04-8c4f-fb8cbbc29f18",
+    "fields": {
+      "created_date": "2019-12-11T22:53:17.802Z",
+      "modified_date": "2020-02-04T15:29:52.626Z",
+      "run": "ca18b090-03ad-4bef-acd3-52600f8e62eb",
+      "name": "db_files",
+      "port_type": 0,
+      "schema": {
+        "type": "record",
+        "fields": {
+          "refseq": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "vep_data": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          },
+          "vep_path": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          },
+          "custom_enst": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          },
+          "facets_snps": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "hotspot_vcf": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          },
+          "fp_genotypes": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "fp_intervals": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "hotspot_list": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          },
+          "delly_exclude": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "bait_intervals": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "conpair_markers": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          },
+          "hotspot_list_maf": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "target_intervals": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "File"
+          },
+          "conpair_markers_bed": {
+            "type": [
+              "null",
+              "array"
+            ],
+            "items": "string"
+          }
+        }
+      },
+      "secondary_files": [],
+      "db_value": {
+        "refseq": {
+          "class": "File",
+          "location": "bid://3814d1da-e507-4e7d-b65b-b4ae7e07ec61"
+        },
+        "vep_data": "/var/cache",
+        "vep_path": "/usr/bin/vep",
+        "custom_enst": "/usr/bin/vcf2maf/data/isoform_overrides_at_mskcc",
+        "facets_snps": {
+          "class": "File",
+          "location": "bid://584079c8-13af-4816-9ad3-50ce86e13934"
+        },
+        "hotspot_vcf": "/usr/bin/basicfiltering/data/hotspot-list-union-v1-v2.vcf",
+        "fp_genotypes": {
+          "class": "File",
+          "location": "bid://1edc9e89-8014-406f-92ee-74084eb981cd"
+        },
+        "fp_intervals": {
+          "class": "File",
+          "location": "bid://b7c7d59f-73a4-4968-bbf5-cd6aed491b91"
+        },
+        "hotspot_list": "/usr/bin/ngs-filters/data/hotspot-list-union-v1-v2.txt",
+        "delly_exclude": {
+          "class": "File",
+          "location": "bid://e0e6283e-24ba-466f-9ec6-b6d9d85b2acd"
+        },
+        "bait_intervals": {
+          "class": "File",
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diff --git a/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.without_disambiguate.json b/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.without_disambiguate.json
new file mode 100644
index 000000000..2520b8bf9
--- /dev/null
+++ b/fixtures/tests/ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.without_disambiguate.json
@@ -0,0 +1,7027 @@
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+      "modified_date": "2019-12-14T12:08:14.394Z",
+      "file": "587840dc-bcb6-48e6-9000-ae912c415d7c",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "75c071df-9735-4f5e-9d11-338364c3f3f5",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.411Z",
+      "modified_date": "2019-12-14T12:08:14.411Z",
+      "file": "c1b4ba39-e14b-4535-88f1-53de2f5809c9",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "6ff6f734-4ac5-4c0b-ae9c-1c66915a4de6",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.432Z",
+      "modified_date": "2019-12-14T12:08:14.432Z",
+      "file": "df4b1eb1-640f-486c-846f-6df01c5896ac",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "9c20e7a4-03be-4764-89bc-e6365f4a2f9a",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.449Z",
+      "modified_date": "2019-12-14T12:08:14.449Z",
+      "file": "5ed9c450-1af5-422c-8c74-f60219ad3d26",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "8c5372f6-7d52-4d58-8ed0-1b3ef7e72a01",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.470Z",
+      "modified_date": "2019-12-14T12:08:14.470Z",
+      "file": "262c3d19-2d59-403d-bd2b-66e0fe404413",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "8a4038d5-9ecb-40ed-a964-1163f83abb17",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.492Z",
+      "modified_date": "2019-12-14T12:08:14.492Z",
+      "file": "dfc3e0f6-be3c-4a1c-a2b7-fc8fb8791b1d",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "c1314a25-14fe-4420-b272-af86064e0d17",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.509Z",
+      "modified_date": "2019-12-14T12:08:14.509Z",
+      "file": "4c1e5a08-70c1-4dcf-9d4b-b4d0d67ba3a7",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "9ba79561-0602-408b-b963-cb46c53658ea",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.532Z",
+      "modified_date": "2019-12-14T12:08:14.532Z",
+      "file": "23fdfe62-3e90-4db4-b533-acadf2f4e1b2",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "cb3bf695-96b9-4fce-a562-48fdad6939f6",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.548Z",
+      "modified_date": "2019-12-14T12:08:14.548Z",
+      "file": "b950c7ff-84c1-4476-99e1-6f0b16626f76",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "95bbe477-eac9-4f0c-8207-d2dc6bfe536f",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.580Z",
+      "modified_date": "2019-12-14T12:08:14.580Z",
+      "file": "3cab6e36-70f5-46df-aa00-a4473cb8ed50",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "41764b23-c374-4d24-884f-5341047ba4e3",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.598Z",
+      "modified_date": "2019-12-14T12:08:14.598Z",
+      "file": "44f519d5-a59c-4baa-bf08-2792460c78bc",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "fb5431c0-e7ae-4ef0-b71c-fa81838977c4",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.621Z",
+      "modified_date": "2019-12-14T12:08:14.621Z",
+      "file": "c211d274-6cf8-4f37-a201-1441b3f3c716",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "13deb153-5dad-4e83-9e61-91262e14e416",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.640Z",
+      "modified_date": "2019-12-14T12:08:14.640Z",
+      "file": "8265b3ed-0c3c-496c-ab97-ea312cf878db",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "e93ee833-0002-4e3e-8cf1-69e81a18c65b",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.663Z",
+      "modified_date": "2019-12-14T12:08:14.663Z",
+      "file": "8b2f58fc-ff9a-4d00-a3e2-c62228b99993",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "a2950c05-2e28-42b2-9594-bf05be09dd09",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.679Z",
+      "modified_date": "2019-12-14T12:08:14.679Z",
+      "file": "995ae979-5156-4107-a9e6-20ce77f59ecb",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "732c5c6a-0e3e-42bf-9ae0-dd014c03ff60",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.702Z",
+      "modified_date": "2019-12-14T12:08:14.702Z",
+      "file": "7ba48896-0c1a-443e-8258-0a35914dfd3e",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "b3af6100-e256-49e3-9e0b-76e4db510f10",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.743Z",
+      "modified_date": "2019-12-14T12:08:14.743Z",
+      "file": "86a62cf2-398d-4876-b195-e2780ba594bd",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "7d8dd044-53eb-4735-982d-d07804986d46",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.766Z",
+      "modified_date": "2019-12-14T12:08:14.766Z",
+      "file": "92c9dd48-f4de-4788-b7a3-fe9ff7d17d7b",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "9957d731-e1ad-431e-a1a9-1fd825dca32a",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.805Z",
+      "modified_date": "2019-12-14T12:08:14.805Z",
+      "file": "b16cea6f-7590-4a4f-9be2-38e045bf60cf",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "5e3401fc-64b8-4047-838d-19cb6589cd63",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.827Z",
+      "modified_date": "2019-12-14T12:08:14.827Z",
+      "file": "3a95e8f1-aa34-4f81-b77a-ab59307c1d5f",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "70889fa4-5c6a-4ee4-9dd4-b7b9a16f6d68",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.848Z",
+      "modified_date": "2019-12-14T12:08:14.848Z",
+      "file": "3f92509f-90a9-4de8-ad81-1d021607918b",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "d81be683-8fb2-42c1-94a7-e4e777bce313",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.865Z",
+      "modified_date": "2019-12-14T12:08:14.865Z",
+      "file": "afe65a89-6272-4276-bb34-d401f41a480a",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  },
+  {
+    "model": "file_system.filemetadata",
+    "pk": "f63728b5-80b8-4783-80a1-2b5c60d51955",
+    "fields": {
+      "created_date": "2019-12-14T12:08:14.887Z",
+      "modified_date": "2019-12-14T12:08:14.887Z",
+      "file": "daef2d43-6a6f-4c3c-9e80-076e3e8288cb",
+      "version": 0,
+      "metadata": {},
+      "user": null,
+      "latest": true
+    }
+  }
+]
\ No newline at end of file
diff --git a/notifier/helper.py b/notifier/helper.py
index bf5357b97..650d7015e 100644
--- a/notifier/helper.py
+++ b/notifier/helper.py
@@ -69,28 +69,11 @@ def generate_sample_data_content(files, pipeline_name, pipeline_github, pipeline
 def generate_sample_data_content_str(metadata, pipeline_name, pipeline_github, pipeline_version, project_id=None):
     if project_id:
         project_id = get_project_id(metadata[settings.REQUEST_ID_METADATA_KEY])
-    result = "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
-        metadata.get(
-            settings.CMO_SAMPLE_TAG_METADATA_KEY,
-            format_sample_name(
-                metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY], metadata[settings.SAMPLE_CLASS_METADATA_KEY]
-            ),
+    sample_id = metadata.get(
+        settings.CMO_SAMPLE_TAG_METADATA_KEY,
+        format_sample_name(
+            metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY], metadata[settings.SAMPLE_CLASS_METADATA_KEY]
         ),
-        metadata[settings.REQUEST_ID_METADATA_KEY],
-        project_id,
-        metadata[settings.PATIENT_ID_METADATA_KEY],
-        metadata["investigatorSampleId"],
-        MetadataValidator.clean_value(metadata[settings.SAMPLE_CLASS_METADATA_KEY]),
-        MetadataValidator.clean_value(metadata[settings.RECIPE_METADATA_KEY]),
-        MetadataValidator.clean_value(metadata[settings.ONCOTREE_METADATA_KEY]),
-        MetadataValidator.clean_value(metadata[settings.SAMPLE_CLASS_METADATA_KEY]),
-        MetadataValidator.clean_value(metadata["preservation"]),
-        MetadataValidator.clean_value(metadata["sex"]),
-        MetadataValidator.clean_value(metadata["tissueLocation"]),
-        metadata[settings.SAMPLE_ID_METADATA_KEY],
-        MetadataValidator.clean_value(metadata["runMode"]),
-        pipeline_name,
-        pipeline_github,
-        pipeline_version,
     )
+    result = f"{sample_id}\t{metadata[settings.REQUEST_ID_METADATA_KEY]}\t{project_id}\t{metadata[settings.PATIENT_ID_METADATA_KEY]}\t{metadata['investigatorSampleId']}\t{MetadataValidator.clean_value(metadata[settings.CMO_SAMPLE_CLASS_METADATA_KEY])}\t{MetadataValidator.clean_value(metadata[settings.RECIPE_METADATA_KEY])}\t{MetadataValidator.clean_value(metadata[settings.ONCOTREE_METADATA_KEY])}\t{MetadataValidator.clean_value(metadata[settings.SAMPLE_CLASS_METADATA_KEY])}\t{MetadataValidator.clean_value(metadata['preservation'])}\t{MetadataValidator.clean_value(metadata['sex'])}\t{MetadataValidator.clean_value(metadata['tissueLocation'])}\t{metadata[settings.SAMPLE_ID_METADATA_KEY]}\t{MetadataValidator.clean_value(metadata['runMode'])}\t{pipeline_name}\t{pipeline_github}\t{pipeline_version}\n"
     return result
diff --git a/runner/fixtures/runner.pipeline.json b/runner/fixtures/runner.pipeline.json
index f342cc6e6..104f10b72 100644
--- a/runner/fixtures/runner.pipeline.json
+++ b/runner/fixtures/runner.pipeline.json
@@ -569,5 +569,21 @@
       "operator": 24,
       "default": true
     }
+  },
+  {
+    "model": "runner.pipeline",
+    "pk": "1e513e56-d500-4d6a-a390-a49f9c5d2379",
+    "fields": {
+      "created_date": "2019-11-18T17:46:45.118Z",
+      "modified_date": "2019-12-05T01:12:39.854Z",
+      "name": "copy_outputs_v1.7.0",
+      "github": "https://github.com/mskcc/argos-cwl",
+      "version": "1.7.0",
+      "entrypoint": "workflows/copy_outputs.cwl",
+      "output_file_group": "a975f490-1b02-4575-abae-a4f8e3667733",
+      "output_directory": "/juno/work/pi/beagle/output/copy_outputs",
+      "operator": 25,
+      "default": true
+    }
   }
 ]
\ No newline at end of file
diff --git a/runner/operator/access/heme/nucleo/heme_nucleo.py b/runner/operator/access/heme/nucleo/heme_nucleo.py
index 8a3ee4a17..a3491bebf 100644
--- a/runner/operator/access/heme/nucleo/heme_nucleo.py
+++ b/runner/operator/access/heme/nucleo/heme_nucleo.py
@@ -7,6 +7,7 @@
 from collections import defaultdict
 
 from runner.operator.operator import Operator
+from runner.operator.helper import pair_samples
 from runner.run.objects.run_creator_object import RunCreator
 from file_system.repository.file_repository import FileRepository
 
@@ -85,7 +86,7 @@ def construct_sample_inputs(samples, request_id):
         sample_group = list(sample_group)
         sample_id = sample_group[0]["metadata"][settings.CMO_SAMPLE_NAME_METADATA_KEY]
 
-        fgbio_fastq_to_bam_input = group_by_run(sample_group)
+        fgbio_fastq_to_bam_input = pair_samples(sample_group)
         fgbio_fastq_to_bam_input = [
             [
                 {"class": "File", "location": "juno://" + s[0]["path"]},
diff --git a/runner/operator/access/heme/qc/heme_nucleo_qc.py b/runner/operator/access/heme/qc/heme_nucleo_qc.py
index 7680839e1..3828069f2 100644
--- a/runner/operator/access/heme/qc/heme_nucleo_qc.py
+++ b/runner/operator/access/heme/qc/heme_nucleo_qc.py
@@ -67,6 +67,7 @@ def get_jobs(self):
                     "name": "Heme Nucleo QC: %s, %i of %i" % (self.request_id, i + 1, len(sample_inputs)),
                     "app": self.get_pipeline_id(),
                     "inputs": job,
+                    "output_metadata": output_metadata,
                     "tags": {settings.REQUEST_ID_METADATA_KEY: self.request_id, "cmoSampleId": job["sample_name"]},
                 }
             )
diff --git a/runner/operator/access/v2_1_0/nucleo/access_nucleo.py b/runner/operator/access/v2_1_0/nucleo/access_nucleo.py
index b4ab6c360..cdefbfea5 100644
--- a/runner/operator/access/v2_1_0/nucleo/access_nucleo.py
+++ b/runner/operator/access/v2_1_0/nucleo/access_nucleo.py
@@ -7,6 +7,7 @@
 from collections import defaultdict
 
 from runner.operator.operator import Operator
+from runner.operator.helper import pair_samples
 from runner.run.objects.run_creator_object import RunCreator
 from file_system.repository.file_repository import FileRepository
 
@@ -45,12 +46,6 @@ def group_by_sample_id(samples):
     return sample_pairs
 
 
-def group_by_run(samples):
-    samples.sort(key=lambda s: s["path"].split("/")[-1])
-    fastqs = zip(samples[::2], samples[1::2])
-    return list(fastqs)
-
-
 def calc_avg(sample_files, field):
     samples_with_field = list(filter(lambda s: field in s["metadata"] and s["metadata"][field], sample_files))
     field_count = len(samples_with_field)
@@ -85,7 +80,7 @@ def construct_sample_inputs(samples, request_id):
         sample_group = list(sample_group)
         sample_id = sample_group[0]["metadata"][settings.CMO_SAMPLE_NAME_METADATA_KEY]
 
-        fgbio_fastq_to_bam_input = group_by_run(sample_group)
+        fgbio_fastq_to_bam_input = pair_samples(sample_group)
         fgbio_fastq_to_bam_input = [
             [
                 {"class": "File", "location": "juno://" + s[0]["path"]},
diff --git a/runner/operator/access/v2_1_0/nucleo/input_template.json.jinja2 b/runner/operator/access/v2_1_0/nucleo/input_template.json.jinja2
index b97e48443..3478229ae 100644
--- a/runner/operator/access/v2_1_0/nucleo/input_template.json.jinja2
+++ b/runner/operator/access/v2_1_0/nucleo/input_template.json.jinja2
@@ -5,6 +5,11 @@
     "platform-model": "novaseq",
     "platform-unit": "{{ barcode_id }}",
 
+    "UBG_abra2_targets": {
+        "class": "File",
+        "location": "juno:///juno/work/access/production/resources/msk-access/v2.0/regions_of_interest/current/MSK-ACCESS-v2_targetsAllwFP_500buffer.bed"
+    },
+
     "BC_picard_fixmate_information_output_file_name": "{{ sample_id }}_cl_aln_srt_MD_IR_FX_BR__aln_srt_IR_FX.bam",
     "UBG_abra2_output_bams": "{{ sample_id }}_uncollapsed_IR.bam",
     "UBG_bwa_mem_output": "{{ sample_id }}_uncollapsed.sam",
diff --git a/runner/operator/argos_bam_operator/v1_1_0/__init__.py b/runner/operator/argos_bam_operator/v1_1_0/__init__.py
new file mode 100644
index 000000000..2bfe8ec33
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/__init__.py
@@ -0,0 +1 @@
+from .argos_bam_operator import ArgosBamOperator
diff --git a/runner/operator/argos_bam_operator/v1_1_0/argos_bam_operator.py b/runner/operator/argos_bam_operator/v1_1_0/argos_bam_operator.py
new file mode 100644
index 000000000..0d41fd429
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/argos_bam_operator.py
@@ -0,0 +1,507 @@
+import os
+from django.conf import settings
+from django.db.models import Q
+from file_system.models import FileGroup
+from runner.operator.operator import Operator
+from runner.run.objects.run_creator_object import RunCreator
+from .construct_argos_pair import construct_argos_jobs, get_project_prefix
+from runner.models import Pipeline
+from notifier.models import JobGroup
+from .bin.make_sample import build_sample
+from notifier.events import (
+    UploadAttachmentEvent,
+    OperatorRequestEvent,
+    CantDoEvent,
+    SetLabelEvent,
+    LocalStoreAttachmentsEvent,
+)
+from notifier.tasks import send_notification
+from notifier.helper import generate_sample_data_content
+from runner.run.processors.file_processor import FileProcessor
+from file_system.repository.file_repository import FileRepository
+from .bin.retrieve_samples_by_query import build_dmp_sample, get_pooled_normal_files, build_pooled_normal_sample_by_file
+from .bin.make_sample import format_sample_name
+
+
+class ArgosBamOperator(Operator):
+    ARGOS_NAME = "argosBam"
+    ARGOS_VERSION = "1.7.0"
+
+    def get_jobs(self):
+
+        argos_jobs = list()
+        dmp_samples = list()
+        if self.pairing:
+            files, cnt_tumors, dmp_samples = self.get_files_for_pairs(self.pairing)
+        elif self.request_id:
+            files, cnt_tumors = self.get_files(self.request_id)
+
+        if cnt_tumors == 0:
+            cant_do = CantDoEvent(self.job_group_notifier_id).to_dict()
+            send_notification.delay(cant_do)
+            all_normals_event = SetLabelEvent(self.job_group_notifier_id, "all_normals").to_dict()
+            send_notification.delay(all_normals_event)
+            return argos_jobs
+
+        data = self.build_data_list(files)
+
+        samples = self.get_samples_from_data(data)
+        argos_inputs, error_samples = construct_argos_jobs(samples, self.pairing, logger=self.logger)
+        sample_pairing = self.get_pairing_from_argos_inputs(argos_inputs)
+        sample_mapping, filepaths = self.get_mapping_from_argos_inputs(argos_inputs)
+        argos_jobs = self.get_argos_jobs(argos_inputs)
+        pipeline = self.get_pipeline_id()
+
+        try:
+            pipeline_obj = Pipeline.objects.get(id=pipeline)
+        except Pipeline.DoesNotExist:
+            pass
+
+        operator_run_summary = UploadAttachmentEvent(
+            self.job_group_notifier_id, "sample_pairing.txt", sample_pairing
+        ).to_dict()
+        send_notification.delay(operator_run_summary)
+        operator_run_summary_local = LocalStoreAttachmentsEvent(
+            self.job_group_notifier_id, "sample_pairing.txt", sample_pairing
+        ).to_dict()
+        send_notification.delay(operator_run_summary_local)
+
+        mapping_file_event = UploadAttachmentEvent(
+            self.job_group_notifier_id, "sample_mapping.txt", sample_mapping
+        ).to_dict()
+        send_notification.delay(mapping_file_event)
+        mapping_file_event_local = LocalStoreAttachmentsEvent(
+            self.job_group_notifier_id, "sample_mapping.txt", sample_mapping
+        ).to_dict()
+        send_notification.delay(mapping_file_event_local)
+
+        data_clinical = generate_sample_data_content(
+            filepaths,
+            pipeline_name=pipeline_obj.name,
+            pipeline_github=pipeline_obj.github,
+            pipeline_version=pipeline_obj.version,
+            dmp_samples=dmp_samples,
+        )
+        sample_data_clinical_event = UploadAttachmentEvent(
+            self.job_group_notifier_id, "sample_data_clinical.txt", data_clinical
+        ).to_dict()
+        send_notification.delay(sample_data_clinical_event)
+        sample_data_clinical_local = LocalStoreAttachmentsEvent(
+            self.job_group_notifier_id, "sample_data_clinical.txt", data_clinical
+        ).to_dict()
+        send_notification.delay(sample_data_clinical_local)
+
+        self.evaluate_sample_errors(error_samples)
+        self.summarize_pairing_info(argos_inputs)
+
+        return argos_jobs
+
+    def get_argos_jobs(self, argos_inputs):
+        argos_jobs = list()
+        number_of_inputs = len(argos_inputs)
+        for i, job in enumerate(argos_inputs):
+            tumor_sample_name = job["tumor"]["ID"]
+            normal_sample_name = job["normal"]["ID"]
+
+            name = "ARGOS BAM %s, %i of %i" % (self.request_id, i + 1, number_of_inputs)
+            assay = job["assay"]
+            pi = job["pi"]
+            pi_email = job["pi_email"]
+
+            tags = {
+                settings.REQUEST_ID_METADATA_KEY: self.request_id,
+                "sampleNameTumor": tumor_sample_name,
+                "sampleNameNormal": normal_sample_name,
+                "labHeadName": pi,
+                "labHeadEmail": pi_email,
+            }
+            pipeline_id = self.get_pipeline_id()
+            pipeline = Pipeline.objects.get(id=pipeline_id)
+            argos_bam_job_data = {"app": pipeline_id, "inputs": job, "name": name, "tags": tags}
+            output_dir = os.path.join(pipeline.output_directory, "argosBam", get_project_prefix(self.request_id))
+            if self.output_directory_prefix:
+                tags["output_directory_prefix"] = self.output_directory_prefix
+                output_dir = os.path.join(pipeline.output_directory, "argosBam", output_prefix)
+            if self.job_group_id:
+                jg = JobGroup.objects.get(id=self.job_group_id)
+                jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+                output_directory = os.path.join(output_dir, pipeline.version, jg_created_date)
+                argos_bam_job_data["output_directory"] = output_directory
+            argos_jobs.append(RunCreator(**argos_bam_job_data))
+        return argos_jobs
+
+    def get_mapping_from_argos_inputs(self, argos_inputs):
+        sample_mapping = ""
+        check_for_duplicates = list()
+        filepaths = list()
+        for i, job in enumerate(argos_inputs):
+            tumor_sample_name = job["tumor"]["ID"]
+            for p in job["tumor"]["R1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["R2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["zR1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["zR2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["bam"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+
+            normal_sample_name = job["normal"]["ID"]
+            for p in job["normal"]["R1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["R2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["zR1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["zR2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["bam"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+        return sample_mapping, filepaths
+
+    def get_pairing_from_argos_inputs(self, argos_inputs):
+        sample_pairing = ""
+        for i, job in enumerate(argos_inputs):
+            tumor_sample_name = job["tumor"]["ID"]
+            normal_sample_name = job["normal"]["ID"]
+            sample_pairing += "\t".join([normal_sample_name, tumor_sample_name]) + "\n"
+        return sample_pairing
+
+    def get_samples_from_data(self, data):
+        samples = list()
+        # group by igoId
+        igo_id_group = dict()
+        for sample in data:
+            igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+            if igo_id not in igo_id_group:
+                igo_id_group[igo_id] = list()
+            igo_id_group[igo_id].append(sample)
+
+        for igo_id in igo_id_group:
+            sample = igo_id_group[igo_id][0]
+            sample_name = sample["metadata"][settings.CMO_SAMPLE_NAME_METADATA_KEY]
+            if "poolednormal" in sample_name.lower():
+                samples.append(build_sample(igo_id_group[igo_id], ignore_sample_formatting=True))
+            else:
+                samples.append(build_sample(igo_id_group[igo_id]))
+        return samples
+
+    def get_files_for_pairs(self, pairing):
+        all_files = []
+        cnt_tumors = 0
+        dmp_samples = list()
+        for pair in pairing.get("pairs"):
+            tumor_sample = pair["tumor"]
+            normal_sample = pair["normal"]
+            tumor, is_dmp_tumor_sample = self.get_regular_sample(tumor_sample, "Tumor")
+            cnt_tumors += 1
+            normal, is_dmp_normal_sample = self.get_regular_sample(normal_sample, "Normal")
+            if not normal and tumor:  # get from pooled normal
+                normal = list()
+                run_ids = list()
+                for t_files in tumor:
+                    run_id = t_files.metadata["runId"]
+                    if run_id:
+                        run_ids.append(run_id)
+                run_ids.sort()
+                tumor_current = tumor.first()
+                bait_set = tumor_current.metadata["baitSet"]
+                preservation_types = tumor_current.metadata["preservation"]
+                pooled_normal_files, bait_set_reformatted, sample_name = get_pooled_normal_files(
+                    run_ids, preservation_types, bait_set
+                )
+                for f in pooled_normal_files:
+                    metadata = build_pooled_normal_sample_by_file(
+                        f, run_ids, preservation_types, bait_set_reformatted, sample_name
+                    )["metadata"]
+                    sample = f
+                    sample.metadata = metadata
+                    normal.append(sample)
+            for file in list(tumor):
+                if file not in all_files:
+                    all_files.append(file)
+                if tumor not in dmp_samples:
+                    if is_dmp_tumor_sample:
+                        dmp_samples.append(tumor)
+            for file in list(normal):
+                if file not in all_files:
+                    all_files.append(file)
+                if normal not in dmp_samples:
+                    if is_dmp_normal_sample:
+                        dmp_samples.append(normal)
+        return all_files, cnt_tumors, dmp_samples
+
+    def get_files(self, request_id):
+        files = FileRepository.filter(
+            queryset=self.files,
+            metadata={settings.REQUEST_ID_METADATA_KEY: request_id, settings.IGO_COMPLETE_METADATA_KEY: True},
+            filter_redact=True,
+        )
+
+        cnt_tumors = FileRepository.filter(
+            queryset=self.files,
+            metadata={
+                settings.REQUEST_ID_METADATA_KEY: request_id,
+                "tumorOrNormal": "Tumor",
+                settings.IGO_COMPLETE_METADATA_KEY: True,
+            },
+            filter_redact=True,
+        ).count()
+        return files, cnt_tumors
+
+    def build_data_list(self, files):
+        data = list()
+        for f in files:
+            sample = dict()
+            sample["id"] = f.file.id
+            sample["path"] = f.file.path
+            sample["file_name"] = f.file.file_name
+            sample["metadata"] = f.metadata
+            data.append(sample)
+        return data
+
+    def get_regular_sample(self, sample_data, tumor_type):
+        sample_id = sample_data["sample_id"]
+        sample = FileRepository.filter(
+            queryset=self.files,
+            metadata={settings.CMO_SAMPLE_TAG_METADATA_KEY: sample_id, settings.IGO_COMPLETE_METADATA_KEY: True},
+            filter_redact=True,
+        )
+        is_dmp_sample = False
+        request_id = self.request_id
+        if not sample:  # try dmp sample
+            if "patient_id" in sample_data:
+                patient_id = sample_data["patient_id"]
+            if "bait_set" in sample_data:
+                bait_set = sample_data["bait_set"]
+            if "pi" in sample_data:
+                pi = sample_data["pi"]
+            if "pi_email" in sample_data:
+                pi_email = sample_data["pi_email"]
+            dmp_bam_id = sample_id.replace("s_", "").replace("_", "-")
+            dmp_bam_slug = Q(file__file_group=FileGroup.objects.get(slug="dmp-bams"))
+            dmp_bam_files = FileRepository.filter(q=dmp_bam_slug)
+            data = FileRepository.filter(queryset=dmp_bam_files, metadata={"external_id": dmp_bam_id})
+            sample = list()
+            if len(data) > 0:
+                s = data[0]
+                metadata = build_dmp_sample(s, patient_id, bait_set, tumor_type, request_id, pi, pi_email)["metadata"]
+                s.metadata = metadata
+                if s:
+                    is_dmp_sample = True
+                    sample.append(s)
+        return sample, is_dmp_sample
+
+    def summarize_pairing_info(self, argos_inputs):
+        num_pairs = len(argos_inputs)
+        num_dmp_normals = 0
+        num_pooled_normals = 0
+        num_outside_req = 0
+        num_within_req = 0
+        other_requests_matched = list()
+        for i, job in enumerate(argos_inputs):
+            tumor = job["tumor"]
+            normal = job["normal"]
+            req_t = tumor["request_id"]
+            req_n = normal["request_id"]
+            specimen_type_n = normal["specimen_type"]
+            if specimen_type_n.lower() in "DMP Normal".lower():
+                num_dmp_normals += 1
+            elif specimen_type_n.lower() in "Pooled Normal".lower():
+                num_pooled_normals += 1
+            elif req_t.strip() != req_n.strip():
+                num_outside_req += 1
+                data = dict()
+                data["sample_name"] = tumor["ID"]
+                data["matched_sample_name"] = normal["ID"]
+                data["normal_request"] = req_n
+                other_requests_matched.append(data)
+            else:
+                num_within_req += 1
+        s = "Number of pairs: %i\n\n" % num_pairs
+        s += "%i samples matched with DMP Normal\n" % num_dmp_normals
+        s += "%i samples matched with pooled normals\n" % num_pooled_normals
+        s += "%i samples matched with normal from different request" % num_outside_req
+
+        if num_outside_req > 0:
+            s += "\n\nMatched samples fom different request\n"
+            s += "| Sample Name | Matched Normal | Request Normal |\n"
+            for i in other_requests_matched:
+                sample_name = i["sample_name"]
+                matched_sample = i["matched_sample_name"]
+                normal_request = i["normal_request"]
+                s += "| %s | %s | %s |\n" % (sample_name, matched_sample, normal_request)
+
+        self.send_message(s)
+
+    def send_message(self, msg):
+        event = OperatorRequestEvent(self.job_group_notifier_id, msg)
+        e = event.to_dict()
+        send_notification.delay(e)
+
+    def evaluate_sample_errors(self, error_samples):
+        s = list()
+        unformatted_s = list()
+        unformatted_s.append("IGO Sample ID\tSample Name / Error\tPatient ID\tSpecimen Type\n")
+        for sample in error_samples:
+            sample_name = sample.get("SM", "missingSampleName")
+            sample_id = sample.get("sample_id", "missingSampleId")
+            patient_id = sample.get("patient_id", "missingPatientId")
+            specimen_type = sample.get("specimen_type", "missingSpecimenType")
+            s.append("| " + sample_id + " | " + sample_name + " |" + patient_id + " |" + specimen_type + " |")
+            unformatted_s.append(sample_id + "\t" + sample_name + "\t" + patient_id + "\t" + specimen_type + "\n")
+
+        msg = """
+        Number of samples with error: {number_of_errors}
+
+        Error samples (also see error_sample_formatting.txt):
+        | IGO Sample ID | Sample Name / Error | Patient ID | Specimen Type |
+        {error_sample_names}
+        """
+
+        msg = msg.format(number_of_errors=str(len(error_samples)), error_sample_names="\n".join(s))
+
+        self.send_message(msg)
+
+        sample_errors_event = UploadAttachmentEvent(
+            self.job_group_notifier_id, "error_sample_formatting.txt", "".join(unformatted_s)
+        ).to_dict()
+        send_notification.delay(sample_errors_event)
+
+    def format_sample_name(self, *args, **kwargs):
+        return format_sample_name(*args, **kwargs)
+
+    def on_job_fail(self, run):
+        cmo_sample_name = run.tags.get("sampleNameTumor")
+        files = FileRepository.filter(queryset=self.files, metadata={"cmoSampleName": cmo_sample_name})
+        if files:
+            qc_report = files[0].metadata["qcReports"]
+            sample_id = files[0].metadata[settings.SAMPLE_ID_METADATA_KEY]
+            """
+            {
+                "comments": "Suboptimal quantity",
+                "qcReportType": "LIBRARY",
+                "IGORecommendation": "Try",
+                "investigatorDecision": "Continue processing"
+            }
+            """
+            report_str = ""
+            for report in qc_report:
+                report_str += "{comments}\t{qc_report_type}\t{igo_recommendation}\t{investigator_decision}\n".format(
+                    comments=report["comments"],
+                    qc_report_type=report["qcReportType"],
+                    igo_recommendation=report["IGORecommendation"],
+                    investigator_decision=report["investigatorDecision"],
+                )
+            msg = """
+cmoSampleId: {cmo_sample_name}
+sampleId: {sample_id}
+Comments\tQC Report Type\tIGORecommendation\tInvestigator Decision\n
+{report_str}
+""".format(
+                cmo_sample_name=cmo_sample_name, sample_id=sample_id, report_str=report_str
+            )
+
+            file_name = "{cmo_sample_name}_igo_qc_report".format(cmo_sample_name=cmo_sample_name)
+            sample_errors_event = UploadAttachmentEvent(self.job_group_notifier_id, file_name, msg).to_dict()
+            send_notification.delay(sample_errors_event)
+
+    def get_log_directory(self):
+        jg = JobGroup.objects.get(id=self.job_group_id)
+        jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+        app = self.get_pipeline_id()
+        pipeline = Pipeline.objects.get(id=app)
+        output_directory = os.path.join(
+            pipeline.output_directory,
+            self.ARGOS_NAME,
+            get_project_prefix(self.request_id),
+            self.ARGOS_VERSION,
+            jg_created_date,
+            "json",
+            pipeline.name,
+            pipeline.version,
+            "%s",
+        )
+        return output_directory
+
+    def get_dmp_samples_from_argos_inputs(self, argos_inputs):
+        dmp_samples = list()
+        for i, job in enumerate(argos_inputs):
+            pi = job["pi"]
+            pi_email = job["pi_email"]
+            patient_id = job["patient_id"]
+            bait_set = job["assay"]
+            tumor_id = job["tumor"]["ID"]
+            normal_id = job["normal"]["ID"]
+            sample_tumor = dict(pi=pi, bait_set=bait_set, pi_email=pi_email, patient_id=patient_id, sample_id=tumor_id)
+            this_sample, is_dmp_sample = self.get_regular_sample(sample_tumor, "Tumor")
+            if is_dmp_sample:
+                dmp_samples.append(this_sample)
+            sample_normal = dict(
+                pi=pi, bait_set=bait_set, pi_email=pi_email, patient_id=patient_id, sample_id=normal_id
+            )
+            this_sample, is_dmp_sample = self.get_regular_sample(sample_normal, "Normal")
+            if is_dmp_sample:
+                dmp_samples.append(this_sample)
+        return dmp_samples
diff --git a/runner/operator/argos_bam_operator/v1_1_0/bin/make_sample.py b/runner/operator/argos_bam_operator/v1_1_0/bin/make_sample.py
new file mode 100644
index 000000000..ed3b85f51
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/bin/make_sample.py
@@ -0,0 +1,372 @@
+"""
+This constructs a sample dictionary from the metadata in the Voyager/Beagle database
+"""
+import logging
+import os
+import re
+from django.conf import settings
+from runner.operator.helper import format_sample_name, get_r_orientation, spoof_barcode
+from file_system.repository.file_repository import FileRepository
+from pprint import pprint
+
+LOGGER = logging.getLogger(__name__)
+
+
+def remove_with_caveats(samples):
+    """
+    Removes samples from a list of samples if they either
+    don't contain a 'sampleNameMalformed', which happens when function
+    format_sample_name returns it
+    """
+    data = list()
+    error_data = list()
+    for sample in samples:
+        add = True
+        sample_id = sample["sample_id"]
+        sample_name = sample["SM"]
+        patient_id = sample["patient_id"]
+        if sample_name == "sampleNameMalformed":
+            add = False
+            LOGGER.info("Sample name is malformed for for %s; removing from set", sample_id)
+        if not patient_id:
+            add = False
+            LOGGER.info("No patient ID for sample %s; removing from set", sample_id)
+        elif isinstance(patient_id, str):
+            if not patient_id.strip():
+                add = False
+                LOGGER.info("Empty string for patient ID in sample %s; removing from set", sample_id)
+        if add:
+            data.append(sample)
+        else:
+            error_data.append(sample)
+    return data, error_data
+
+
+def check_and_return_single_values(data):
+    """
+    data is a dictionary; each key contains a list of values.
+
+    single_values are the expected keys that should contain only one value
+
+    Concatenating pi and pi_email AND formatting the LB field are workarounds
+    because some samples would have multiple values for these but the sample dict
+    it returns must have one value only in order for the pipeline to execute
+    """
+    single_values = [
+        "CN",
+        "PL",
+        "SM",
+        "bait_set",
+        "patient_id",
+        "species",
+        "tumor_type",
+        "sample_id",
+        "specimen_type",
+        "request_id",
+        "run_mode",
+    ]
+
+    for key in single_values:
+        value = set(data[key])
+        if len(value) == 1:
+            data[key] = value.pop()
+        else:
+            LOGGER.error("Expected only one value for %s!", key)
+            LOGGER.error("Check import, something went wrong.")
+
+    # concatenating pi and pi_email
+    if data["pi"] == [None]:
+        data["pi"] = [""]
+    if data["pi_email"] == [None]:
+        data["pi_email"] = [""]
+    data["pi"] = "; ".join(set(data["pi"]))
+    data["pi_email"] = "; ".join(set(data["pi_email"]))
+
+    # hack; formats LB field so that it is a string
+    library_id = [i for i in data["LB"] if i]
+    number_of_library_ids = len(library_id)
+    if number_of_library_ids > 0:
+        library_id.sort()
+        data["LB"] = "_and_".join(library_id)
+    else:
+        data["LB"] = data["SM"] + "_1"
+
+    # run_ids need to be one list
+    run_ids = data["run_id"]
+    if any(isinstance(run_id, list) for run_id in run_ids):
+        run_ids = [item for sublist in run_ids for item in sublist]
+        data["run_id"] = list(set(run_ids))
+    return data
+
+
+def get_file(fpath):
+    files = FileRepository.all()
+    data = FileRepository.filter(queryset=files, path=fpath)
+    if data:
+        return data[0]
+    return None
+
+
+def get_run_mode(run_mode):
+    if isinstance(run_mode, str):
+        if "hiseq" in run_mode.lower():
+            return "hiseq"
+        if "novaseq" in run_mode.lower():
+            return "novaseq"
+        return run_mode
+
+
+def build_sample(data, ignore_sample_formatting=False):
+    """
+    Given some data - which is a list of samples originally from the LIMS, split up into one file
+    per index - the data is then compiled into one sample dictionary consisting of one or more
+    pairs of fastqs
+
+    Note that ID and SM are different field values in ARGOS (RG_ID and ID, respectively, in ARGOS)
+    but standardizing it here with what GATK sets bam headers to
+    """
+
+    samples = dict()
+
+    for value in data:
+        fpath = value["path"]
+        curr_file = get_file(fpath)
+        meta = value["metadata"]
+        bid = value["id"]
+        sequencing_center = meta["sequencingCenter"]
+        platform = meta["platform"]
+        request_id = meta[settings.REQUEST_ID_METADATA_KEY]
+        sample_id = meta[settings.SAMPLE_ID_METADATA_KEY]
+        library_id = meta[settings.LIBRARY_ID_METADATA_KEY]
+        bait_set = meta["baitSet"]
+        tumor_type = meta["tumorOrNormal"]
+        specimen_type = meta[settings.SAMPLE_CLASS_METADATA_KEY]
+        species = meta["species"]
+        cmo_sample_name = format_sample_name(
+            meta[settings.CMO_SAMPLE_NAME_METADATA_KEY], specimen_type, ignore_sample_formatting
+        )
+        if cmo_sample_name == "sampleNameMalformed":
+            LOGGER.error("sampleName for %s is malformed", sample_id)
+        flowcell_id = meta["flowCellId"]
+        barcode_index = meta["barcodeIndex"]
+        cmo_patient_id = meta[settings.PATIENT_ID_METADATA_KEY]
+        platform_unit = flowcell_id
+        run_date = meta["runDate"]
+        r_orientation = meta["R"]
+        pi_name = meta.get("pi", meta["labHeadName"])
+        pi_email = meta.get("pi_email", meta["labHeadEmail"])
+        run_id = meta["runId"]
+        preservation_type = meta["preservation"]
+        rg_id = cmo_sample_name + "_1"
+        run_mode = get_run_mode(meta["runMode"])
+        if barcode_index:
+            platform_unit = "_".join([flowcell_id, barcode_index])
+        try:
+            rg_id = "_".join([cmo_sample_name, platform_unit])
+        except:
+            LOGGER.info("RG ID couldn't be set.")
+            LOGGER.info("Sample ID %s; patient ID %s", sample_id, cmo_patient_id)
+            LOGGER.info("SampleName %s; platform unit %s", cmo_sample_name, platform_unit)
+        if sample_id not in samples:
+            samples[sample_id] = dict()
+            sample = dict()
+            sample["CN"] = sequencing_center
+            sample["PL"] = platform
+            sample["PU"] = list()
+            sample["LB"] = library_id
+            sample["tumor_type"] = tumor_type
+            sample["SM"] = cmo_sample_name
+            sample["species"] = species
+            sample["patient_id"] = cmo_patient_id
+            sample["bait_set"] = bait_set
+            sample["sample_id"] = sample_id
+            sample["run_date"] = run_date
+            sample["specimen_type"] = specimen_type
+            sample["request_id"] = request_id
+            sample["pi"] = pi_name
+            sample["pi_email"] = pi_email
+            sample["run_id"] = run_id
+            sample["preservation_type"] = preservation_type
+            sample["ID"] = list()
+            sample["R1"] = list()
+            sample["R1_bid"] = list()
+            sample["R2"] = list()
+            sample["R2_bid"] = list()
+            sample["fastqs"] = list()
+            sample["run_mode"] = run_mode
+        else:
+            sample = samples[sample_id]
+
+        # Queueing up fastqs for pairing later; RG ID and PU
+        # will be assigned based on Fastqs object
+        if "R1" in r_orientation or "R2" in r_orientation:
+            sample["fastqs"].append(curr_file)
+        else:
+            # DMP bams found; assigning RG ID and PU here
+            # There will always be only one DMP bam, so assign explicitly
+            sample["bam"] = fpath
+            sample["bam_bid"] = bid
+            sample["PU"] = platform_unit
+            sample["ID"] = rg_id
+        samples[sample_id] = sample
+
+    result = dict()
+    result["CN"] = list()
+    result["PL"] = list()
+    result["PU"] = list()
+    result["LB"] = list()
+    result["tumor_type"] = list()
+    result["ID"] = list()
+    result["SM"] = list()
+    result["species"] = list()
+    result["patient_id"] = list()
+    result["bait_set"] = list()
+    result["sample_id"] = list()
+    result["run_date"] = list()
+    result["specimen_type"] = list()
+    result["R1"] = list()
+    result["R2"] = list()
+    result["R1_bid"] = list()
+    result["R2_bid"] = list()
+    result["bam"] = list()
+    result["bam_bid"] = list()
+    result["request_id"] = list()
+    result["pi"] = list()
+    result["pi_email"] = list()
+    result["run_id"] = list()
+    result["preservation_type"] = list()
+    result["run_mode"] = list()
+
+    for sample_id in samples:
+        sample = samples[sample_id]
+        for key in sample:
+            if key == "fastqs":
+                if sample["fastqs"]:
+                    fastqs = Fastqs(sample["SM"], sample["fastqs"])
+                    result["R1"] = fastqs.r1
+                    result["R1_bid"] = fastqs.r1_bids
+                    result["R2"] = fastqs.r2
+                    result["R2_bid"] = fastqs.r2_bids
+                    result["PU"] = fastqs.pu
+                    result["ID"] = fastqs.rg_id
+            else:
+                result[key].append(sample[key])
+    result = check_and_return_single_values(result)
+    return result
+
+
+class Fastqs:
+    """
+    Fastqs class to hold pairs of fastqs
+
+    Does the pairing from a list of files
+
+    The paired bool is True if all of the R1s in file list find a matching R2
+    """
+
+    def __init__(self, sample_name, file_list):
+        self.sample_name = sample_name
+        self.fastqs = dict()
+        self.r1 = list()
+        self.r2 = list()
+        self.r2_bids = list()
+        self.paired = True
+        self._set_R(file_list)
+        self.r1_bids = self._set_bids(self.r1)
+        self.r2_bids = self._set_bids(self.r2)
+        self.pu = self._set_pu()
+        self.rg_id = self._set_rg_id()
+
+    def _set_bids(self, r):
+        r_bids = list()
+        for f in r:
+            r_file = get_file(f)
+            r_bids.append(r_file.id)
+        return r_bids
+
+    def _set_pu(self):
+        """
+        Creating a list of PU values; used by argos pipeline as scatter input
+
+        Only iterating across r1s since r1 and r2 should have the same metadata
+        """
+        pu = list()
+        for f in self.r1:
+            metadata = get_file(f).metadata
+            flowcell_id = "MT_FCID"
+            if "poolednormal" in self.sample_name.lower():
+                flowcell_id = "PN_FCID"
+                r = get_r_orientation(f)
+                barcode_index = spoof_barcode(os.path.basename(f), r)
+            else:
+                fid = metadata["flowCellId"]
+                if fid:
+                    flowcell_id = fid
+                barcode_index = metadata["barcodeIndex"]
+            platform_unit = flowcell_id
+            if barcode_index:
+                platform_unit = "_".join([flowcell_id, barcode_index])
+            pu.append(platform_unit)
+        return pu
+
+    def _set_rg_id(self):
+        """
+        Creating a list of RG_ID values; used by argos pipeline as scatter input
+
+        Only iterating across r1s since r1 and r2 should have the same metadata
+        """
+        rg_ids = list()
+        for i, f in enumerate(self.r1):
+            metadata = get_file(f).metadata
+            sample_name = self.sample_name
+            pu = self.pu[i]
+            rg_id = "_".join([sample_name, pu])
+            rg_ids.append(rg_id)
+        return rg_ids
+
+    def _set_R(self, file_list):
+        """
+        From the file list, retrieve R1 and R2 fastq files
+
+        Sets PU and bids, as well
+
+        Uses _get_fastq_from_list() to find R2 pair.
+        """
+        r1s = list()
+        r2s = list()
+        for i in file_list:
+            f = i.file
+            r = get_r_orientation(f.path)
+            if r == "R1":
+                r1s.append(f)
+            if r == "R2":
+                r2s.append(f)
+        for f in r1s:
+            self.r1.append(f.path)
+            fastq1 = f.path
+            expected_r2 = "R2".join(fastq1.rsplit("R1", 1))
+            fastq2 = self._get_fastq_from_list(expected_r2, r2s)
+            if fastq2:
+                self.r2.append(fastq2.path)
+            else:
+                print("No fastq R2 found for %s" % f.path)
+                self.paired = False
+
+    def __str__(self):
+        s = "R1:\n"
+        for i in self.r1:
+            s += i.path + "\n"
+        s += "\nR2:\n"
+        for i in self.r2:
+            s += i.path + "\n"
+        return s
+
+    def _get_fastq_from_list(self, fastq_path, fastq_files):
+        """
+        Given fastq_path, find it in the list of fastq_files and return
+        that File object
+        """
+        for f in fastq_files:
+            fpath = f.path
+            if fastq_path == fpath:
+                return f
diff --git a/runner/operator/argos_bam_operator/v1_1_0/bin/pair_request.py b/runner/operator/argos_bam_operator/v1_1_0/bin/pair_request.py
new file mode 100644
index 000000000..c87caa9b2
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/bin/pair_request.py
@@ -0,0 +1,260 @@
+"""
+Given a request, this module will attempt to find a pair for every tumor within that request.
+
+It performs the following:
+
+  - Checks if a normal exists within that same request
+  - Checks if a normal exists in a different request for that same patient
+  - Checks if a DMP bam will be within that request (unimplemented as of 2020-03-03; upcoming)
+  - Checks if a pooled normal exists
+
+Normals will have to have the same patient and bait set in order to be considered "viable"
+"""
+import logging
+from datetime import datetime as dt
+from .retrieve_samples_by_query import get_samples_from_patient_id, get_pooled_normals, get_dmp_bam
+
+LOGGER = logging.getLogger(__name__)
+
+
+def get_by_tumor_type(data, tumor_type):
+    """
+    Retrieves a set of samples that contain a value tumor_type
+
+    tumor_tupe is typically Normal or Tumor
+    """
+    samples = list()
+    for sample in data:
+        if tumor_type.lower() in sample["tumor_type"].lower():
+            samples.append(sample)
+    return samples
+
+
+def compare_dates(normal, viable_normal, date_string):
+    """
+    Compares dates between two normals; returns the most recent
+    """
+    for run_date in normal["run_date"]:
+        normal_date = dt.strptime(run_date, date_string)
+        for vrun_date in viable_normal["run_date"]:
+            vnormal_date = dt.strptime(vrun_date, date_string)
+            if vnormal_date < normal_date:
+                viable_normal = normal
+    return viable_normal
+
+
+def get_viable_normal(normals, patient_id, run_mode, bait_set):
+    """
+    From a set of normals, return the ones that have matching patient_id, bait_set,
+    run_mode, and the most recent
+
+    Does not check for Pooled Normals; that's done separately
+    """
+    viable_normal = dict()
+    for normal in normals:
+        normal_run_mode = get_run_mode(normal["run_mode"])
+        if normal["patient_id"] == patient_id and normal["bait_set"] == bait_set and normal_run_mode == run_mode:
+            if viable_normal:
+                try:
+                    viable_normal = compare_dates(normal, viable_normal, "%y-%m-%d")
+                except ValueError:
+                    LOGGER.debug("Trying different date parser")
+                    viable_normal = compare_dates(normal, viable_normal, "%Y-%m-%d")
+            else:
+                viable_normal = normal
+    return viable_normal
+
+
+def compile_pairs(samples, pairing_info=None, logger=None):
+    """
+    Creates pairs of tumors and normals from a list of samples
+    """
+    tumors = get_by_tumor_type(samples, "Tumor")
+    normals = get_by_tumor_type(samples, "Normal")
+
+    # pairing
+    pairs = dict()
+    pairs["tumor"] = list()
+    pairs["normal"] = list()
+
+    num_tumors = len(tumors)
+    if num_tumors == 0:
+        LOGGER.error("No tumor samples found; pairing will not be performed.")
+        logger.log("No tumor samples found; pairing will not be performed.") if logger else None
+        LOGGER.error("Returning an empty list of pairs.")
+        logger.log("Returning an empty list of pairs.") if logger else None
+
+    for tumor in tumors:
+        LOGGER.info("Pairing tumor sample %s", tumor["sample_id"])
+        logger.log("Returning an empty list of pairs.") if logger else None
+        if pairing_info:
+            """
+            Creating samples, based on predefined pairing list
+            """
+            for pair in pairing_info["pairs"]:
+                tumor_sample = pair["tumor"]
+                if tumor["SM"] == tumor_sample["sample_id"]:
+                    for normal in normals:
+                        normal_sample = pair["normal"]
+                        if "poolednormal" in normal_sample["sample_id"].lower():
+                            """
+                            We should have a pool normal to pair with here
+                            """
+                            normal_run_ids = normal["run_id"]
+                            normal_bait_set = normal["bait_set"]
+                            normal_preservation = [x.lower() for x in normal["preservation_type"]]
+                            if all(elem in normal_run_ids for elem in tumor["run_id"]):
+                                """
+                                Checks if any of the run ids in the pre-created normal samples
+                                are also all in the tumor sample
+                                """
+                                if normal_bait_set.lower() in tumor["bait_set"].lower():
+                                    tumor_preservations = [x.lower() for x in tumor["preservation_type"]]
+                                    # Must check if FFPE or other
+                                    if "ffpe" in normal_preservation:
+                                        if "ffpe" in tumor_preservations:
+                                            pairs["tumor"].append(tumor)
+                                            pairs["normal"].append(normal)
+                                            break
+                                    else:
+                                        if "ffpe" not in tumor_preservations:
+                                            pairs["tumor"].append(tumor)
+                                            pairs["normal"].append(normal)
+                                            break
+                        elif normal["SM"] == normal_sample["sample_id"]:
+                            """
+                            This pairs with a dmp normal
+                            """
+                            pairs["tumor"].append(tumor)
+                            pairs["normal"].append(normal)
+                            break
+        else:
+            patient_id = tumor["patient_id"]
+            if patient_id:
+                run_mode = get_run_mode(tumor["run_mode"])
+                bait_set = tumor["bait_set"]
+                run_ids = tumor["run_id"]
+                preservation_types = tumor["preservation_type"]
+                normal = get_viable_normal(normals, patient_id, run_mode, bait_set)
+                if normal:
+                    LOGGER.info(
+                        "Pairing %s (%s) with %s (%s)",
+                        tumor["sample_id"],
+                        tumor["SM"],
+                        normal["sample_id"],
+                        normal["SM"],
+                    )
+                    logger.log(
+                        f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {normal['sample_id']} ({normal['SM']})"
+                    ) if logger else None
+                    pairs["tumor"].append(tumor)
+                    pairs["normal"].append(normal)
+                else:
+                    LOGGER.info(
+                        "Missing normal for sample %s (%s); querying patient %s",
+                        tumor["sample_id"],
+                        tumor["SM"],
+                        patient_id,
+                    )
+                    logger.log(
+                        f"Missing normal for sample {tumor['sample_id']} ({tumor['SM']}); querying patient {patient_id}"
+                    ) if logger else None
+                    patient_samples = get_samples_from_patient_id(patient_id)
+                    new_normals = get_by_tumor_type(patient_samples, "Normal")
+                    new_normal = get_viable_normal(new_normals, patient_id, run_mode, bait_set)
+                    if new_normal:
+                        LOGGER.info(
+                            "Pairing %s (%s) with %s (%s)",
+                            tumor["sample_id"],
+                            tumor["SM"],
+                            new_normal["sample_id"],
+                            new_normal["SM"],
+                        )
+                        logger.log(
+                            f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {new_normal['sample_id']} ({new_normal['SM']})"
+                        ) if logger else None
+                        pairs["tumor"].append(tumor)
+                        pairs["normal"].append(new_normal)
+                    else:
+                        LOGGER.info("No normal found for patient %s; checking for DMP Normal", patient_id)
+                        logger.log(
+                            f"No normal found for patient {patient_id}; checking for DMP Normal"
+                        ) if logger else None
+                        dmp_normal = get_dmp_bam(patient_id, bait_set, "Normal")
+                        if dmp_normal:
+                            LOGGER.info(
+                                "Pairing %s (%s) with %s (%s)",
+                                tumor["sample_id"],
+                                tumor["SM"],
+                                dmp_normal["sample_id"],
+                                dmp_normal["SM"],
+                            )
+                            logger.log(
+                                f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {dmp_normal['sample_id']} ({dmp_normal['SM']})"
+                            ) if logger else None
+                            pairs["tumor"].append(tumor)
+                            pairs["normal"].append(dmp_normal)
+                        else:
+                            pooled_normal = get_pooled_normals(run_ids, preservation_types, bait_set)
+                            LOGGER.info("No DMP Normal found for patient %s; checking for Pooled Normal", patient_id)
+                            logger.log(
+                                f"No DMP Normal found for patient %s; checking for Pooled Normal {patient_id}"
+                            ) if logger else None
+                            if pooled_normal:
+                                LOGGER.info(
+                                    "Pairing %s (%s) with %s (%s)",
+                                    tumor["sample_id"],
+                                    tumor["SM"],
+                                    pooled_normal["sample_id"],
+                                    pooled_normal["SM"],
+                                )
+                                logger.log(
+                                    f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {pooled_normal['sample_id']} ({pooled_normal['SM']})"
+                                ) if logger else None
+                                pairs["tumor"].append(tumor)
+                                pairs["normal"].append(pooled_normal)
+                            else:
+                                LOGGER.error(
+                                    "No normal found for %s (%s), patient %s",
+                                    tumor["sample_id"],
+                                    tumor["SM"],
+                                    patient_id,
+                                )
+                                logger.log(
+                                    f"No normal found for {tumor['sample_id']} ({tumor['SM']}), patient {patient_id}"
+                                ) if logger else None
+            else:
+                LOGGER.error(
+                    "NoPatientIdError: No patient_id found for %s (%s); skipping.", tumor["sample_id"], tumor["SM"]
+                )
+                logger.log(
+                    f"NoPatientIdError: No patient_id found for {tumor['sample_id']} ({tumor['SM']}); skipping."
+                ) if logger else None
+
+    return pairs
+
+
+def get_run_mode(run_mode):
+    """
+    Normalizing hiseq and novaseq strings
+    """
+    if run_mode:
+        if "hiseq" in run_mode.lower():
+            return "hiseq"
+        if "novaseq" in run_mode.lower():
+            return "novaseq"
+    return run_mode
+
+
+def create_pairing_info(pairs):
+    """
+    Outputs pairing data in the form of TUMOR\tNORMAL
+    Used in legacy Tempo and Roslin
+    """
+    output_string = ""
+    num_tumors = len(pairs["tumor"])
+    for i in range(0, num_tumors):
+        tumor_name = pairs["tumor"][i]["SM"]
+        normal_name = pairs["normal"][i]["SM"]
+        output_string += "\t".join([normal_name, tumor_name]) + "\n"
+    return output_string
diff --git a/runner/operator/argos_bam_operator/v1_1_0/bin/retrieve_samples_by_query.py b/runner/operator/argos_bam_operator/v1_1_0/bin/retrieve_samples_by_query.py
new file mode 100644
index 000000000..711f05a9c
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/bin/retrieve_samples_by_query.py
@@ -0,0 +1,357 @@
+"""
+Functions for retrieving samples from the database based on other criteria
+that wasn't in the operator
+
+For example, get all samples by a patient ID or pooled normals based on the bait set
+and preservation type
+"""
+import logging
+from file_system.models import MachineRunMode
+from file_system.models import FileGroup
+from file_system.repository.file_repository import FileRepository
+from django.db.models import Q
+from django.conf import settings
+from .make_sample import build_sample, remove_with_caveats, format_sample_name
+from runner.operator.helper import get_r_orientation, spoof_barcode, init_metadata
+
+LOGGER = logging.getLogger(__name__)
+
+
+def build_argos_file_groups_query():
+    ARGOS_FG_SLUGS = ["lims", "origin-unknown"]
+    slug_set = [Q(file__file_group=FileGroup.objects.get(slug=value)) for value in set(ARGOS_FG_SLUGS)]
+    query = slug_set.pop()
+    for item in slug_set:
+        query |= item
+    return query
+
+
+def get_samples_from_patient_id(patient_id):
+    """
+    Retrieves samples from the database based on the patient_id
+
+    Only retrieve patients from LIMS file group
+    """
+    all_files = FileRepository.all()
+    q_pid = Q(("metadata__{}".format(settings.PATIENT_ID_METADATA_KEY), patient_id))
+    q_fg = build_argos_file_groups_query()
+    q = q_pid & q_fg
+    files = FileRepository.filter(queryset=all_files, q=q)
+    files = FileRepository.filter(queryset=files, filter_redact=True)
+    data = list()
+    for current_file in files:
+        sample = dict()
+        sample["id"] = current_file.file.id
+        sample["path"] = current_file.file.path
+        sample["file_name"] = current_file.file.file_name
+        sample["metadata"] = current_file.metadata
+        data.append(sample)
+
+    samples = list()
+    # group by igoId
+    igo_id_group = dict()
+    for sample in data:
+        igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+        if igo_id not in igo_id_group:
+            igo_id_group[igo_id] = list()
+        igo_id_group[igo_id].append(sample)
+
+    for igo_id in igo_id_group:
+        samples.append(build_sample(igo_id_group[igo_id]))
+    samples, bad_samples = remove_with_caveats(samples)
+    number_of_bad_samples = len(bad_samples)
+    if number_of_bad_samples > 0:
+        LOGGER.warning("Some samples for patient query %s have invalid %i values", patient_id, number_of_bad_samples)
+    return samples
+
+
+def get_descriptor(bait_set, pooled_normals, preservation_types, run_ids):
+    """
+    Need descriptor to match pooled normal "genePanel", which might need to be re-labeled as bait_set
+
+    Adding correction for IMPACT505 pooled normals
+    """
+    query = Q(file__file_group=settings.POOLED_NORMAL_FILE_GROUP)
+    sample_name = None
+
+    descriptor = None
+    for pooled_normal in pooled_normals:
+        bset_data = pooled_normal.metadata[settings.RECIPE_METADATA_KEY]
+        if bset_data.lower() in bait_set.lower():
+            descriptor = bset_data
+
+    if descriptor:  # From returned pooled normals, we found the bait set/genePanel we're looking for
+        pooled_normals = FileRepository.filter(
+            queryset=pooled_normals, metadata={settings.RECIPE_METADATA_KEY: descriptor}
+        )
+
+        # sample_name is FROZENPOOLEDNORMAL unless FFPE is in any of the preservation types
+        # in preservation_types
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        run_ids_suffix_list = [i for i in run_ids if i]  # remove empty or false string values
+        run_ids_suffix = "_".join(set(run_ids_suffix_list))
+        sample_name = "FROZENPOOLEDNORMAL_" + run_ids_suffix
+        if "ffpe" in preservations_lower_case:
+            sample_name = "FFPEPOOLEDNORMAL_" + run_ids_suffix
+    elif "impact505" in bait_set.lower():
+        # We didn't find a pooled normal for IMPACT505; return "static" FROZEN or FFPE pool normal
+        descriptor = "IMPACT505"
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        machine = get_sequencer_type(run_ids)
+        if not machine:
+            LOGGER.error("Could not find IMPACT505 pooled normal for $s; new machine name?", sample_name)
+        if machine == "hiseq":
+            sample_name = "FROZENPOOLEDNORMAL_IMPACT505_V1"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_IMPACT505_V1"
+        if machine == "novaseq":
+            sample_name = "FROZENPOOLEDNORMAL_IMPACT505_V2"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_IMPACT505_V2"
+        q = query & Q(("metadata__{}".format(settings.SAMPLE_NAME_METADATA_KEY), sample_name))
+        pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+        if not pooled_normals:
+            LOGGER.error("Could not find IMPACT505 pooled normal to pair %s", sample_name)
+    elif "hemepact_v4" in bait_set.lower():
+        # We didn't find a pooled normal for HemePACT_v4; return "static" FROZEN or FFPE pool normal
+        descriptor = "HemePACT_v4"
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        machine = get_sequencer_type(run_ids)
+        if not machine:
+            LOGGER.error("Could not find HemePACT_v4 pooled normal for $s; new machine name?", sample_name)
+        if machine == "hiseq":
+            sample_name = "FROZENPOOLEDNORMAL_HemePACT_v4_V1"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_HemePACT_v4_V1"
+        if machine == "novaseq":
+            sample_name = "FROZENPOOLEDNORMAL_HemePACT_v4_V2"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_HemePACT_v4_V2"
+        q = query & Q(("metadata__{}".format(settings.SAMPLE_NAME_METADATA_KEY), sample_name))
+        pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+        if not pooled_normals:
+            LOGGER.error("Could not find HemePACT_v4 pooled normal to pair %s", sample_name)
+    elif "impact-heme_v2" in bait_set.lower():
+        # We didn't find a pooled normal for IMPACT-Heme_v2; return "static" FROZEN or FFPE pool normal
+        descriptor = "IMPACT-Heme_v2"
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        machine = get_sequencer_type(run_ids)
+        if not machine:
+            LOGGER.error("Could not find IMPACT-Heme_v2 pooled normal for $s; new machine name?", sample_name)
+        if machine == "novaseq":
+            sample_name = "FROZENPOOLEDNORMAL_IMPACT-Heme_v2_V1"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_IMPACT-Heme_v2_V1"
+        q = query & Q(("metadata__{}".format(settings.SAMPLE_NAME_METADATA_KEY), sample_name))
+        pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+        if not pooled_normals:
+            LOGGER.error("Could not find IMPACT-Heme_v2 pooled normal to pair %s", sample_name)
+
+    return pooled_normals, descriptor, sample_name
+
+
+def get_sequencer_type(run_ids_list):
+    run_ids_lower = [i.lower() for i in run_ids_list if i]
+    machine_modes = MachineRunMode.objects.all()
+    for machine in machine_modes:
+        if find_substr(machine.machine_name, run_ids_lower):
+            return machine.machine_class
+
+
+def find_substr(s, l):
+    return any(s in string for string in l)
+
+
+def build_run_id_query(data):
+    """
+    Build complex Q object run id query from given data
+
+    Only does OR queries, as seen in line
+
+       query |= item
+
+    Very similar to build_preservation_query, but "filemetadata__metadata__runId"
+    can't be sent as a value, so had to make a semi-redundant function
+    """
+    data_query_set = [Q(metadata__runId=value) for value in set(data)]
+    query = data_query_set.pop()
+    for item in data_query_set:
+        query |= item
+    return query
+
+
+def build_preservation_query(data):
+    """
+    Build simple query for either FROZEN or FFPE pooled normal
+
+    Main logic: if FFPE in data, return FFPE query; else, return FROZEN query
+    """
+    preservations_lower_case = set([x.lower() for x in data])
+    value = "FROZEN"
+    if "ffpe" in preservations_lower_case:
+        value = "FFPE"
+    # case-insensitive matching
+    query = Q(metadata__preservation__iexact=value)
+    return query
+
+
+def get_pooled_normals(run_ids, preservation_types, bait_set):
+    """
+    From a list of run_ids, preservation types, and bait sets, get all potential pooled normals
+    """
+    pooled_normals, descriptor, sample_name = get_pooled_normal_files(run_ids, preservation_types, bait_set)
+    sample_files = list()
+    for pooled_normal_file in pooled_normals:
+        sample_file = build_pooled_normal_sample_by_file(
+            pooled_normal_file, run_ids, preservation_types, descriptor, sample_name
+        )
+        sample_files.append(sample_file)
+    pooled_normal = build_sample(sample_files, ignore_sample_formatting=True)
+
+    return pooled_normal
+
+
+def get_pooled_normal_files(run_ids, preservation_types, bait_set):
+
+    pooled_normals = FileRepository.all()
+
+    query = Q(file__file_group=settings.POOLED_NORMAL_FILE_GROUP)
+    run_id_query = build_run_id_query(run_ids)
+    preservation_query = build_preservation_query(preservation_types)
+
+    q = query & run_id_query & preservation_query
+
+    pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+
+    pooled_normals, descriptor, sample_name = get_descriptor(bait_set, pooled_normals, preservation_types, run_ids)
+
+    return pooled_normals, descriptor, sample_name
+
+
+def build_pooled_normal_sample_by_file(pooled_normal, run_ids, preservation_types, bait_set, sample_name):
+    specimen_type = "Pooled Normal"
+    sample = dict()
+    sample["id"] = pooled_normal.file.id
+    sample["path"] = pooled_normal.file.path
+    sample["file_name"] = pooled_normal.file.file_name
+    metadata = init_metadata()
+    metadata[settings.SAMPLE_ID_METADATA_KEY] = sample_name
+    metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY] = sample_name
+    metadata[settings.CMO_SAMPLE_TAG_METADATA_KEY] = sample_name
+    metadata[settings.REQUEST_ID_METADATA_KEY] = sample_name
+    metadata["sequencingCenter"] = "MSKCC"
+    metadata["platform"] = "Illumina"
+    metadata["baitSet"] = bait_set
+    metadata[settings.RECIPE_METADATA_KEY] = bait_set
+    metadata["runId"] = run_ids
+    metadata["preservation"] = preservation_types
+    metadata[settings.LIBRARY_ID_METADATA_KEY] = sample_name + "_1"
+    # because rgid depends on flowCellId and barcodeIndex, we will
+    # spoof barcodeIndex so that pairing can work properly; see
+    # build_sample in runner.operator.argos_operator.bin
+    metadata["R"] = get_r_orientation(pooled_normal.file.file_name)
+    metadata["barcodeIndex"] = spoof_barcode(sample["file_name"], metadata["R"])
+    metadata["flowCellId"] = "PN_FCID"
+    metadata["tumorOrNormal"] = "Normal"
+    metadata[settings.PATIENT_ID_METADATA_KEY] = "PN_PATIENT_ID"
+    metadata[settings.SAMPLE_CLASS_METADATA_KEY] = specimen_type
+    metadata["runMode"] = ""
+    metadata[settings.CMO_SAMPLE_CLASS_METADATA_KEY] = ""
+    sample["metadata"] = metadata
+    return sample
+
+
+def get_dmp_bam(patient_id, bait_set, tumor_type):
+    """
+    From a patient id and bait set, get matching dmp bam normal
+    """
+    file_objs = FileRepository.all()
+
+    dmp_query = build_dmp_query(patient_id, bait_set)
+
+    dmp_bam = FileRepository.filter(queryset=file_objs, q=dmp_query).order_by("file__file_name").first()
+
+    if dmp_bam:
+        sample = build_dmp_sample(dmp_bam, patient_id, bait_set, tumor_type)
+        built_sample = build_sample([sample], ignore_sample_formatting=True)
+        return built_sample
+    return None
+
+
+def build_dmp_sample(dmp_bam, patient_id, bait_set, tumor_type, request_id=None, pi=None, pi_email=None):
+
+    dmp_metadata = dmp_bam.metadata
+    specimen_type = "DMP"
+    sample_name = dmp_metadata["external_id"]
+    sequencingCenter = "MSKCC"
+    platform = "Illumina"
+    sample = dict()
+    sample["id"] = dmp_bam.file.id
+    sample["path"] = dmp_bam.file.path
+    sample["file_name"] = dmp_bam.file.file_name
+    sample["file_type"] = dmp_bam.file.file_type
+    metadata = init_metadata()
+    metadata[settings.SAMPLE_ID_METADATA_KEY] = format_sample_name(sample_name, specimen_type)
+    metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY] = format_sample_name(sample_name, specimen_type)
+    metadata[settings.CMO_SAMPLE_TAG_METADATA_KEY] = metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY]
+    metadata[settings.REQUEST_ID_METADATA_KEY] = request_id
+    metadata["request_id"] = request_id
+    metadata["investigatorSampleId"] = dmp_metadata["sample"]
+    metadata["sequencingCenter"] = sequencingCenter
+    metadata["platform"] = platform
+    metadata["baitSet"] = bait_set
+    metadata[settings.RECIPE_METADATA_KEY] = bait_set
+    metadata["run_id"] = ""
+    metadata["preservation"] = ""
+    metadata[settings.LIBRARY_ID_METADATA_KEY] = sample_name + "_1"
+    metadata["R"] = "Not applicable"
+    # because rgid depends on flowCellId and barcodeIndex, we will
+    # spoof barcodeIndex so that pairing can work properly; see
+    # build_sample in runner.operator.argos_operator.bin
+    metadata["barcodeIndex"] = "DMP_BARCODEIDX"
+    metadata["flowCellId"] = "DMP_FCID"
+    metadata["tumorOrNormal"] = tumor_type
+    metadata["sampleType"] = tumor_type
+    metadata[settings.PATIENT_ID_METADATA_KEY] = patient_id
+    metadata[settings.SAMPLE_CLASS_METADATA_KEY] = specimen_type
+    metadata["runMode"] = ""
+    metadata["sex"] = ""
+    metadata["tissueLocation"] = dmp_metadata.get("tissue_type", "")
+    metadata[settings.ONCOTREE_METADATA_KEY] = dmp_metadata.get("tumor_type", "")
+    metadata[settings.CMO_SAMPLE_CLASS_METADATA_KEY] = ""
+    if pi:
+        metadata["pi"] = pi
+    if pi_email:
+        metadata["pi_email"] = pi_email
+    sample["metadata"] = metadata
+    return sample
+
+
+def build_dmp_query(patient_id, bait_set):
+    """
+    Build simple Q queries for patient id, bait set, and type 'N' to signify "normal"
+
+    The bait set from file groups/LIMS is different from what's in DMP, so this
+    translates it.
+
+    Patient ID in DMP also doesn't contain C-, so this removes that prefix
+    """
+    value = ""
+    if "impact341" in bait_set.lower():
+        value = "IMPACT341"
+    if "impact410" in bait_set.lower():
+        value = "IMPACT410"
+    if "impact468" in bait_set.lower():
+        value = "IMPACT468"
+    if "hemepact_v4" in bait_set.lower():
+        value = "HEMEPACT"
+    if "impact505" in bait_set.lower():
+        value = "IMPACT505"
+    assay = Q(metadata__cmo_assay=value)
+    # formatting to look like CMO patient IDs in dmp2cmo
+    if "C-" in patient_id[:2]:
+        patient_id = patient_id[2:]
+    patient = Q(metadata__patient__cmo=patient_id)
+    normal = Q(metadata__type="N")
+    query = assay & patient & normal
+    return query
diff --git a/runner/operator/argos_bam_operator/v1_1_0/construct_argos_pair.py b/runner/operator/argos_bam_operator/v1_1_0/construct_argos_pair.py
new file mode 100644
index 000000000..0b82a2298
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/construct_argos_pair.py
@@ -0,0 +1,224 @@
+import re
+import json
+import os, sys
+import logging
+from pprint import pprint
+from django.conf import settings
+from .bin.make_sample import remove_with_caveats
+from .bin.pair_request import compile_pairs
+from file_system.repository.file_repository import FileRepository
+
+WORKDIR = os.path.dirname(os.path.abspath(__file__))
+LOGGER = logging.getLogger(__name__)
+
+
+PDX_SPECIMEN_TYPES = ["pdx", "xenograft", "xenograftderivedcellline"]
+NON_PDX_SPECIMEN_TYPES = [
+    "biopsy",
+    "blood",
+    "cellLine",
+    "cfdna",
+    "fingernails",
+    "nonpdx",
+    "normal",
+    "organoid",
+    "other",
+    "rapidautopsy",
+    "resection",
+    "saliva",
+    "tumor",
+    "poolednormal",
+    "dmp",
+]
+
+# TODO: generalize
+def load_references():
+    d = json.load(open(os.path.join(WORKDIR, "reference_jsons/genomic_resources.json"), "rb"))
+    return d
+
+
+def normalize_igo_text_field(igo_text):
+    # Flatten text data from the Genomics Core
+    # to allow robust exact text matching.
+    #
+    # Allow variance in case and ignore non
+    # alphanumeric characters (FYI).
+    # Convert to lowercase
+    s = igo_text.lower()
+    # Remove special characters and extra spaces
+    s = re.sub(r"[^a-z0-9]+", "", s)
+    return s
+
+
+# TODO: This is ARGOS-formatted, note the confusing IDs
+def format_sample(data):
+    specimen_type = normalize_igo_text_field(data["specimen_type"])
+    sample = dict()
+    sample["ID"] = data["SM"]  # TODO: change someday
+    sample["CN"] = data["CN"]
+    sample["LB"] = data["LB"]
+    sample["PL"] = data["PL"]
+    sample["PU"] = data["PU"]
+    sample["R1"] = list()
+    sample["R2"] = list()
+    sample["zR1"] = list()  # TODO: Add for Xenografts
+    sample["zR2"] = list()  # TODO: Add for Xenografts
+    sample["bam"] = list()
+    sample["RG_ID"] = data["ID"]
+    sample["adapter"] = "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG"
+    sample["adapter2"] = "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT"
+    sample["bwa_output"] = sample["ID"] + ".bam"
+    sample["request_id"] = data["request_id"]
+    sample["specimen_type"] = data["specimen_type"]
+
+    if specimen_type in PDX_SPECIMEN_TYPES:
+        r1 = "zR1"
+        r2 = "zR2"
+    elif specimen_type in NON_PDX_SPECIMEN_TYPES:
+        r1 = "R1"
+        r2 = "R2"
+    else:
+        raise Exception(f"Invalid Specimen Type: {specimen_type}")
+
+    for i in data["R1"]:
+        if i:
+            sample[r1].append({"class": "File", "location": "juno://" + i})
+    for i in data["R2"]:
+        if i:
+            sample[r2].append({"class": "File", "location": "juno://" + i})
+    for i in data["bam"]:
+        if i:
+            sample["bam"].append({"class": "File", "location": "juno://" + i})
+    return sample
+
+
+def construct_argos_jobs(samples, pairs=None, logger=None):
+    samples, error_samples = remove_with_caveats(samples)
+    pairs = compile_pairs(samples, pairs, logger=logger)
+    number_of_tumors = len(pairs["tumor"])
+    argos_jobs = list()
+    for i in range(0, number_of_tumors):
+        tumor = pairs["tumor"][i]
+        normal = pairs["normal"][i]
+        assay = tumor["bait_set"]
+        patient_id = tumor["patient_id"]
+        pi = tumor["pi"]
+        pi_email = tumor["pi_email"]
+        job = dict()
+        normal_sample = format_sample(normal)
+        tumor_sample = format_sample(tumor)
+        job["tumor"] = tumor_sample
+        job["normal"] = normal_sample
+        job["assay"] = assay
+        job["pi"] = pi
+        job["pi_email"] = pi_email
+        job["patient_id"] = patient_id
+        references = convert_references(assay)
+        job.update(references)
+        argos_jobs.append(job)
+    return argos_jobs, error_samples
+
+
+def get_baits_and_targets(assay, genomic_resources):
+    # probably need similar rules for whatever "Exome" string is in request
+    targets = genomic_resources["targets"]
+
+    target_assay = assay
+
+    if assay.find("HemePACT_v4") > -1:
+        target_assay = "HemePACT_v4_BAITS"
+
+    if assay.find("IMPACT-Heme_v2") > -1:
+        target_assay = "IMPACT-Heme_v2_BAITS"
+
+    if assay.find("IMPACT505") > -1:
+        target_assay = "IMPACT505_b37"
+    if assay.find("IMPACT410") > -1:
+        target_assay = "IMPACT410_b37"
+    if assay.find("IMPACT468") > -1:
+        target_assay = "IMPACT468_b37"
+    if assay.find("IMPACT341") > -1:
+        target_assay = "IMPACT341_b37"
+    if assay.find("IDT_Exome_v1_FP") > -1:
+        target_assay = "IDT_Exome_v1_FP_b37"
+    if assay.find("IMPACT468+08390") > -1:
+        target_assay = "IMPACT468_08390"
+    if assay.find("IMPACT468+Poirier_RB1_intron_V2") > -1:
+        target_assay = "IMPACT468_08050"
+
+    if target_assay in targets:
+        return {
+            "bait_intervals": {"class": "File", "location": str(targets[target_assay]["baits_list"])},
+            "target_intervals": {"class": "File", "location": str(targets[target_assay]["targets_list"])},
+            "fp_intervals": {"class": "File", "location": str(targets[target_assay]["FP_intervals"])},
+        }
+    else:
+        LOGGER.error("ERROR: Targets for Assay not found in genomic_resources.json: %s", assay)
+
+
+def get_facets_cval(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 50
+    return 100
+
+
+def get_facets_pcval(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 100
+    return 500
+
+
+def get_complex_nn(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 0.2
+    return 0.1
+
+
+def get_complex_tn(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 0.5
+    return 0.2
+
+
+def convert_references(assay):
+    genomic_resources = load_references()
+    request_files = genomic_resources["request_files"]
+    intervals = get_baits_and_targets(assay, genomic_resources)
+    covariates = ["CycleCovariate", "ContextCovariate", "ReadGroupCovariate", "QualityScoreCovariate"]
+    genome = "GRCh37"
+    out_dict = {
+        "genome": genome,
+        "intervals": genomic_resources["genomes"][genome]["intervals"],
+        "opt_dup_pix_dist": "2500",
+        "hapmap": {"class": "File", "location": str(request_files["hapmap"])},
+        "dbsnp": {"class": "File", "location": str(request_files["dbsnp"])},
+        "indels_1000g": {"class": "File", "location": str(request_files["indels_1000g"])},
+        "snps_1000g": {"class": "File", "location": str(request_files["snps_1000g"])},
+        "covariates": covariates,
+        "abra_ram_min": 84000,
+        "gatk_jar_path": "/usr/bin/gatk.jar",
+        "conpair_markers_bed": str(request_files["conpair_markers_bed"]),
+        "ref_fasta": {"class": "File", "location": str(request_files["ref_fasta"])},
+        "mouse_fasta": {"class": "File", "location": str(request_files["mouse_fasta"])},
+    }
+    out_dict.update(intervals)
+    return out_dict
+
+
+def get_project_prefix(request_id):
+    project_prefix = set()
+    tumors = FileRepository.filter(
+        metadata={settings.REQUEST_ID_METADATA_KEY: request_id, "tumorOrNormal": "Tumor"},
+        filter_redact=True,
+        values_metadata=settings.REQUEST_ID_METADATA_KEY,
+    )
+    for tumor in tumors:
+        project_prefix.add(tumor)
+    return "_".join(sorted(project_prefix))
+
+
+if __name__ == "__main__":
+    request_id = sys.argv[1]
+
+    argos_jobs = construct_argos_jobs(request_id)
+    pprint(argos_jobs)
diff --git a/runner/operator/argos_bam_operator/v1_1_0/reference_jsons/genomic_resources.json b/runner/operator/argos_bam_operator/v1_1_0/reference_jsons/genomic_resources.json
new file mode 100644
index 000000000..febd20d77
--- /dev/null
+++ b/runner/operator/argos_bam_operator/v1_1_0/reference_jsons/genomic_resources.json
@@ -0,0 +1,328 @@
+{
+    "genomes": {
+        "GRCh37": {
+            "bedtools_genome": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/bedtools_genome/human.b37.genome",
+            "bwa_fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta",
+            "cosmic": {
+                "67": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/cosmic/67/CosmicCodingMuts_v67_b37_20131024__NDS.vcf",
+                "74": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/cosmic/74/CosmicCodingMuts_v74_b37_20151120.vcf"
+            },
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+        "indels_1000g": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf",
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+        "cosmic": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/cosmic/CosmicCodingMuts_v67_b37_20131024__NDS.vcf",
+        "refseq": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/refseq/refGene_b37.sorted.txt",
+        "exac_filter": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/vep/cache/ExAC_nonTCGA.r0.3.1.sites.vep.vcf.gz",
+        "vep_data": "/var/cache",
+        "conpair_markers": "/usr/bin/conpair/data/markers/GRCh37.autosomes.phase3_shapeit2_mvncall_integrated.20130502.SNV.genotype.sselect_v4_MAF_0.4_LD_0.8.txt",
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+        "curated_bams": {
+            "IMPACT468_b37": [
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006710_N001_d.Group5.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006713_N001_d.Group10.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006716_N001_d.Group4.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_006718_N001_d.Group1.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_32WX55_N001_d.Group17.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_DJE4C0_N001_d.Group8.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_FWK8C0_N001_d.Group8.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_HRE3UJ_N001_d.Group9.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_LC80PX_N001_d.Group7.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_LNNV52_N001_d.Group26.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_NJHH88_N001_d.Group16.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_NR3W32_N001_d.Group21.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_P10A8A_N001_d.Group29.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_U53DXH_N001_d.Group1.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_U8A7CR_N001_d.Group14.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_VXC8TK_N001_d.Group24.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_W0TXP0_N001_d.Group22.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_WWTK32_N001_d.Group18.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT468_b37/s_C_XXC7N6_N001_d.Group12.rg.md.abra.printreads.bam"
+            ],
+            "AgilentExon_51MB_b37_v3": [
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000131_N002_d.Group15.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000132_N001_d.Group23.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000133_N001_d.Group6.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000134_N001_d.Group16.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000137_N001_d.Group17.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000138_N001_d.Group22.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000140_N001_d.Group20.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000141_N001_d.Group4.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000146_N001_d.Group8.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_000147_N001_d.Group0.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_001839_N001_d.Group29.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_001841_N001_d.Group23.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_001849_N001_d.Group10.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_001850_N001_d.Group18.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_001851_N002_d.Group35.rg.md.abra.printreads.bam",
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/AgilentExon_51MB_b37_v3/s_C_001855_N001_d.Group2.rg.md.abra.printreads.bam",
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+            ],
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006639_N001_d.Group6.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006640_N001_d.Group7.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006641_N001_d.Group4.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006642_N001_d.Group5.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006643_N001_d.Group2.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006644_N001_d.Group3.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006645_N001_d.Group0.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006646_N001_d.Group1.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006647_N001_d.Group18.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006648_N001_d.Group11.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006649_N001_d.Group10.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006650_N001_d.Group21.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006904_N001_d.Group0.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006905_N001_d.Group1.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006906_N001_d.Group1.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006907_N001_d.Group0.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_006996_N001_d.Group0.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_0AEE89_N001_d.Group0.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_1NPV4P_N001_d.Group0.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_4W32NJ_N001_d.Group1.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_H9KJFX_N001_d.Group2.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_P5FLLT_N001_d.Group4.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_VC7LNE_N001_d.Group5.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IDT_Exome_v1_FP_b37/s_C_WV53F0_N001_d.Group0.rg.md.abra.printreads.bam"
+            ],
+            "IMPACT-Heme_v2_BAITS": [
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_2AL2HX_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_40ADHA_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_46TPL2_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_5F4KFT_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_675JR5_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_FUH2NN_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_HA6MRW_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_HNL3MK_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_K5TJ3T_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_MWEAE4_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_PY8UJ4_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_T4XAKT_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_TLV4D4_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_ULPUE1_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_V16J6D_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_WPM78R_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_XN0L0N_T001_d02.rg.md.abra.printreads.bam"
+            ]
+        },
+        "hotspot_list": "/usr/bin/ngs-filters/data/hotspot-list-union-v1-v2.txt",
+        "hotspot_list_maf": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/qc_resources/hotspot-list-union-v1-v2.maf",
+        "hotspot_vcf": "/usr/bin/basicfiltering/data/hotspot-list-union-v1-v2.vcf",
+        "custom_enst": "/usr/bin/vcf2maf/data/isoform_overrides_at_mskcc",
+        "vep_path": "/usr/bin/vep",
+        "ref_fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta",
+        "mouse_fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCm38/GRCm38.fasta"
+    }
+}
\ No newline at end of file
diff --git a/runner/operator/argos_operator/v2_1_0/__init__.py b/runner/operator/argos_operator/v2_1_0/__init__.py
new file mode 100644
index 000000000..de41d4582
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/__init__.py
@@ -0,0 +1 @@
+from .argos_operator import ArgosOperator
diff --git a/runner/operator/argos_operator/v2_1_0/argos_operator.py b/runner/operator/argos_operator/v2_1_0/argos_operator.py
new file mode 100644
index 000000000..c12713ca9
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/argos_operator.py
@@ -0,0 +1,497 @@
+import os
+from django.conf import settings
+from django.db.models import Q
+from file_system.models import FileGroup
+from runner.operator.operator import Operator
+from runner.run.objects.run_creator_object import RunCreator
+from .construct_argos_pair import construct_argos_jobs, get_project_prefix
+from runner.models import Pipeline
+from .bin.make_sample import build_sample
+from notifier.models import JobGroup, JobGroupNotifier
+from notifier.events import (
+    UploadAttachmentEvent,
+    OperatorRequestEvent,
+    CantDoEvent,
+    SetLabelEvent,
+    LocalStoreAttachmentsEvent,
+)
+from notifier.tasks import send_notification
+from notifier.helper import generate_sample_data_content
+from runner.run.processors.file_processor import FileProcessor
+from file_system.repository.file_repository import FileRepository
+from .bin.retrieve_samples_by_query import build_dmp_sample, get_pooled_normal_files, build_pooled_normal_sample_by_file
+from .bin.make_sample import format_sample_name
+
+
+class ArgosOperator(Operator):
+    ARGOS_NAME = "argos"
+    ARGOS_VERSION = "1.7.0"
+
+    def get_jobs(self):
+
+        argos_jobs = list()
+        dmp_samples = list()
+        if self.pairing:
+            files, cnt_tumors, dmp_samples = self.get_files_for_pairs(self.pairing)
+        elif self.request_id:
+            files, cnt_tumors = self.get_files(self.request_id)
+
+        if cnt_tumors == 0:
+            cant_do = CantDoEvent(self.job_group_notifier_id).to_dict()
+            send_notification.delay(cant_do)
+            all_normals_event = SetLabelEvent(self.job_group_notifier_id, "all_normals").to_dict()
+            send_notification.delay(all_normals_event)
+            return argos_jobs
+
+        data = self.build_data_list(files)
+
+        samples = self.get_samples_from_data(data)
+        argos_inputs, error_samples = construct_argos_jobs(samples, self.pairing, logger=self.logger)
+        sample_pairing = self.get_pairing_from_argos_inputs(argos_inputs)
+        sample_mapping, filepaths = self.get_mapping_from_argos_inputs(argos_inputs)
+        argos_jobs = self.get_argos_jobs(argos_inputs)
+        pipeline = self.get_pipeline_id()
+
+        try:
+            pipeline_obj = Pipeline.objects.get(id=pipeline)
+        except Pipeline.DoesNotExist:
+            pass
+
+        operator_run_summary_local = LocalStoreAttachmentsEvent(
+            self.job_group_notifier_id, "sample_pairing.txt", sample_pairing
+        ).to_dict()
+        send_notification.delay(operator_run_summary_local)
+        mapping_file_event_local = LocalStoreAttachmentsEvent(
+            self.job_group_notifier_id, "sample_mapping.txt", sample_mapping
+        ).to_dict()
+        send_notification.delay(mapping_file_event_local)
+        data_clinical = generate_sample_data_content(
+            filepaths,
+            pipeline_name=pipeline_obj.name,
+            pipeline_github=pipeline_obj.github,
+            pipeline_version=pipeline_obj.version,
+            dmp_samples=dmp_samples,
+        )
+        sample_data_clinical_local = LocalStoreAttachmentsEvent(
+            self.job_group_notifier_id, "sample_data_clinical.txt", data_clinical
+        ).to_dict()
+        send_notification.delay(sample_data_clinical_local)
+        self.evaluate_sample_errors(error_samples)
+        self.summarize_pairing_info(argos_inputs)
+
+        return argos_jobs
+
+    def get_argos_jobs(self, argos_inputs):
+        argos_jobs = list()
+        number_of_inputs = len(argos_inputs)
+        for i, job in enumerate(argos_inputs):
+            tumor_sample_name = job["tumor"]["ID"]
+            normal_sample_name = job["normal"]["ID"]
+
+            name = "ARGOS %s, %i of %i" % (self.request_id, i + 1, number_of_inputs)
+            assay = job["assay"]
+            pi = job["pi"]
+            pi_email = job["pi_email"]
+
+            tags = {
+                settings.REQUEST_ID_METADATA_KEY: self.request_id,
+                "sampleNameTumor": tumor_sample_name,
+                "sampleNameNormal": normal_sample_name,
+                "labHeadName": pi,
+                "labHeadEmail": pi_email,
+            }
+            pipeline = self.get_pipeline_id()
+            if self.output_directory_prefix:
+                tags["output_directory_prefix"] = self.output_directory_prefix
+            argos_jobs.append(RunCreator(app=pipeline, inputs=job, name=name, tags=tags))
+        return argos_jobs
+
+    def get_mapping_from_argos_inputs(self, argos_inputs):
+        sample_mapping = ""
+        check_for_duplicates = list()
+        filepaths = list()
+        for i, job in enumerate(argos_inputs):
+            tumor_sample_name = job["tumor"]["ID"]
+            for p in job["tumor"]["R1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["R2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["zR1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["zR2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["tumor"]["bam"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([tumor_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+
+            normal_sample_name = job["normal"]["ID"]
+            for p in job["normal"]["R1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["R2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["zR1"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["zR2"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+            for p in job["normal"]["bam"]:
+                filepath = FileProcessor.parse_path_from_uri(p["location"])
+                file_str = "\t".join([normal_sample_name, filepath]) + "\n"
+                if file_str not in check_for_duplicates:
+                    check_for_duplicates.append(file_str)
+                    sample_mapping += file_str
+                if filepath not in filepaths:
+                    filepaths.append(filepath)
+        return sample_mapping, filepaths
+
+    def get_pairing_from_argos_inputs(self, argos_inputs):
+        sample_pairing = ""
+        for i, job in enumerate(argos_inputs):
+            tumor_sample_name = job["tumor"]["ID"]
+            normal_sample_name = job["normal"]["ID"]
+            sample_pairing += "\t".join([normal_sample_name, tumor_sample_name]) + "\n"
+        return sample_pairing
+
+    def get_samples_from_data(self, data):
+        samples = list()
+        # group by igoId
+        igo_id_group = dict()
+        for sample in data:
+            igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+            if igo_id not in igo_id_group:
+                igo_id_group[igo_id] = list()
+            igo_id_group[igo_id].append(sample)
+
+        for igo_id in igo_id_group:
+            sample = igo_id_group[igo_id][0]
+            sample_name = sample["metadata"][settings.CMO_SAMPLE_NAME_METADATA_KEY]
+            if "poolednormal" in sample_name.lower():
+                samples.append(build_sample(igo_id_group[igo_id], ignore_sample_formatting=True))
+            else:
+                samples.append(build_sample(igo_id_group[igo_id]))
+        return samples
+
+    def get_files_for_pairs(self, pairing):
+        all_files = []
+        cnt_tumors = 0
+        dmp_samples = list()
+        for pair in pairing.get("pairs"):
+            tumor_sample = pair["tumor"]
+            normal_sample = pair["normal"]
+            tumor, is_dmp_tumor_sample = self.get_regular_sample(tumor_sample, "Tumor")
+            cnt_tumors += 1
+            normal, is_dmp_normal_sample = self.get_regular_sample(normal_sample, "Normal")
+            if not normal and tumor:  # get from pooled normal
+                normal = list()
+                run_ids = list()
+                for t_files in tumor:
+                    run_id = t_files.metadata["runId"]
+                    if run_id:
+                        run_ids.append(run_id)
+                run_ids.sort()
+                tumor_current = tumor.first()
+                bait_set = tumor_current.metadata["baitSet"]
+                preservation_types = tumor_current.metadata["preservation"]
+                pooled_normal_files, bait_set_reformatted, sample_name = get_pooled_normal_files(
+                    run_ids, preservation_types, bait_set
+                )
+                for f in pooled_normal_files:
+                    metadata = build_pooled_normal_sample_by_file(
+                        f, run_ids, preservation_types, bait_set_reformatted, sample_name
+                    )["metadata"]
+                    sample = f
+                    sample.metadata = metadata
+                    normal.append(sample)
+            for file in list(tumor):
+                if file not in all_files:
+                    all_files.append(file)
+                if tumor not in dmp_samples:
+                    if is_dmp_tumor_sample:
+                        dmp_samples.append(tumor)
+            for file in list(normal):
+                if file not in all_files:
+                    all_files.append(file)
+                if normal not in dmp_samples:
+                    if is_dmp_normal_sample:
+                        dmp_samples.append(normal)
+        return all_files, cnt_tumors, dmp_samples
+
+    def get_files(self, request_id):
+        files = FileRepository.filter(
+            queryset=self.files,
+            metadata={settings.REQUEST_ID_METADATA_KEY: request_id, settings.IGO_COMPLETE_METADATA_KEY: True},
+            filter_redact=True,
+        )
+
+        cnt_tumors = FileRepository.filter(
+            queryset=self.files,
+            metadata={
+                settings.REQUEST_ID_METADATA_KEY: request_id,
+                "tumorOrNormal": "Tumor",
+                settings.IGO_COMPLETE_METADATA_KEY: True,
+            },
+            filter_redact=True,
+        ).count()
+        return files, cnt_tumors
+
+    def build_data_list(self, files):
+        data = list()
+        for f in files:
+            sample = dict()
+            sample["id"] = f.file.id
+            sample["path"] = f.file.path
+            sample["file_name"] = f.file.file_name
+            sample["metadata"] = f.metadata
+            data.append(sample)
+        return data
+
+    def get_regular_sample(self, sample_data, tumor_type):
+        sample_id = sample_data["sample_id"]
+        sample = FileRepository.filter(
+            queryset=self.files,
+            metadata={settings.CMO_SAMPLE_TAG_METADATA_KEY: sample_id, settings.IGO_COMPLETE_METADATA_KEY: True},
+            filter_redact=True,
+        )
+        is_dmp_sample = False
+        request_id = self.request_id
+        if not sample:  # try dmp sample
+            if "patient_id" in sample_data:
+                patient_id = sample_data["patient_id"]
+            if "bait_set" in sample_data:
+                bait_set = sample_data["bait_set"]
+            if "pi" in sample_data:
+                pi = sample_data["pi"]
+            if "pi_email" in sample_data:
+                pi_email = sample_data["pi_email"]
+            dmp_bam_id = sample_id.replace("s_", "").replace("_", "-")
+            dmp_bam_slug = Q(file__file_group=FileGroup.objects.get(slug="dmp-bams"))
+            dmp_bam_files = FileRepository.filter(q=dmp_bam_slug)
+            data = FileRepository.filter(queryset=dmp_bam_files, metadata={"external_id": dmp_bam_id})
+            sample = list()
+            if len(data) > 0:
+                s = data[0]
+                metadata = build_dmp_sample(s, patient_id, bait_set, tumor_type, request_id, pi, pi_email)["metadata"]
+                s.metadata = metadata
+                if s:
+                    is_dmp_sample = True
+                    sample.append(s)
+        return sample, is_dmp_sample
+
+    def summarize_pairing_info(self, argos_inputs):
+        num_pairs = len(argos_inputs)
+        num_dmp_normals = 0
+        num_pooled_normals = 0
+        num_outside_req = 0
+        num_within_req = 0
+        other_requests_matched = list()
+        for i, job in enumerate(argos_inputs):
+            tumor = job["tumor"]
+            normal = job["normal"]
+            req_t = tumor["request_id"]
+            req_n = normal["request_id"]
+            specimen_type_n = normal["specimen_type"]
+            if specimen_type_n.lower() in "DMP Normal".lower():
+                num_dmp_normals += 1
+            elif specimen_type_n.lower() in "Pooled Normal".lower():
+                num_pooled_normals += 1
+            elif req_t.strip() != req_n.strip():
+                num_outside_req += 1
+                data = dict()
+                data["sample_name"] = tumor["ID"]
+                data["matched_sample_name"] = normal["ID"]
+                data["normal_request"] = req_n
+                other_requests_matched.append(data)
+            else:
+                num_within_req += 1
+        s = "Number of pairs: %i\n\n" % num_pairs
+        s += "%i samples matched with DMP Normal\n" % num_dmp_normals
+        s += "%i samples matched with pooled normals\n" % num_pooled_normals
+        s += "%i samples matched with normal from different request" % num_outside_req
+
+        if num_outside_req > 0:
+            s += "\n\nMatched samples fom different request\n"
+            s += "| Sample Name | Matched Normal | Request Normal |\n"
+            for i in other_requests_matched:
+                sample_name = i["sample_name"]
+                matched_sample = i["matched_sample_name"]
+                normal_request = i["normal_request"]
+                s += "| %s | %s | %s |\n" % (sample_name, matched_sample, normal_request)
+
+        self.send_message(s)
+
+    def send_message(self, msg):
+        event = OperatorRequestEvent(self.job_group_notifier_id, msg)
+        e = event.to_dict()
+        send_notification.delay(e)
+
+    def evaluate_sample_errors(self, error_samples):
+        s = list()
+        unformatted_s = list()
+        unformatted_s.append("IGO Sample ID\tSample Name / Error\tPatient ID\tSpecimen Type\n")
+        for sample in error_samples:
+            sample_name = sample.get("SM", "missingSampleName")
+            sample_id = sample.get("sample_id", "missingSampleId")
+            patient_id = sample.get("patient_id", "missingPatientId")
+            specimen_type = sample.get("specimen_type", "missingSpecimenType")
+            s.append("| " + sample_id + " | " + sample_name + " |" + patient_id + " |" + specimen_type + " |")
+            unformatted_s.append(sample_id + "\t" + sample_name + "\t" + patient_id + "\t" + specimen_type + "\n")
+
+        msg = """
+        Number of samples with error: {number_of_errors}
+
+        Error samples (also see error_sample_formatting.txt):
+        | IGO Sample ID | Sample Name / Error | Patient ID | Specimen Type |
+        {error_sample_names}
+        """
+
+        msg = msg.format(number_of_errors=str(len(error_samples)), error_sample_names="\n".join(s))
+
+        self.send_message(msg)
+
+        sample_errors_event = UploadAttachmentEvent(
+            self.job_group_notifier_id, "error_sample_formatting.txt", "".join(unformatted_s)
+        ).to_dict()
+        send_notification.delay(sample_errors_event)
+
+    def format_sample_name(self, *args, **kwargs):
+        return format_sample_name(*args, **kwargs)
+
+    def on_job_fail(self, run):
+        cmo_sample_name = run.tags.get("sampleNameTumor")
+        files = FileRepository.filter(queryset=self.files, metadata={"cmoSampleName": cmo_sample_name})
+        if files:
+            qc_report = files[0].metadata["qcReports"]
+            sample_id = files[0].metadata[settings.SAMPLE_ID_METADATA_KEY]
+            """
+            {
+                "comments": "Suboptimal quantity",
+                "qcReportType": "LIBRARY",
+                "IGORecommendation": "Try",
+                "investigatorDecision": "Continue processing"
+            }
+            """
+            report_str = ""
+            for report in qc_report:
+                report_str += "{comments}\t{qc_report_type}\t{igo_recommendation}\t{investigator_decision}\n".format(
+                    comments=report["comments"],
+                    qc_report_type=report["qcReportType"],
+                    igo_recommendation=report["IGORecommendation"],
+                    investigator_decision=report["investigatorDecision"],
+                )
+            msg = """
+cmoSampleId: {cmo_sample_name}
+sampleId: {sample_id}
+Comments\tQC Report Type\tIGORecommendation\tInvestigator Decision\n
+{report_str}
+""".format(
+                cmo_sample_name=cmo_sample_name, sample_id=sample_id, report_str=report_str
+            )
+
+            file_name = "{cmo_sample_name}_igo_qc_report".format(cmo_sample_name=cmo_sample_name)
+            sample_errors_event = UploadAttachmentEvent(self.job_group_notifier_id, file_name, msg).to_dict()
+            send_notification.delay(sample_errors_event)
+
+    def get_log_directory(self):
+        jg = JobGroup.objects.get(id=self.job_group_id)
+        jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+        app = self.get_pipeline_id()
+        pipeline = Pipeline.objects.get(id=app)
+        output_directory = os.path.join(
+            pipeline.output_directory,
+            self.ARGOS_NAME,
+            get_project_prefix(self.request_id),
+            self.ARGOS_VERSION,
+            jg_created_date,
+            "json",
+            pipeline.name,
+            pipeline.version,
+            "%s",
+        )
+        return output_directory
+
+    def get_dmp_samples_from_argos_inputs(self, argos_inputs):
+        dmp_samples = list()
+        for i, job in enumerate(argos_inputs):
+            pi = job["pi"]
+            pi_email = job["pi_email"]
+            patient_id = job["patient_id"]
+            bait_set = job["assay"]
+            tumor_id = job["tumor"]["ID"]
+            normal_id = job["normal"]["ID"]
+            sample_tumor = dict(pi=pi, bait_set=bait_set, pi_email=pi_email, patient_id=patient_id, sample_id=tumor_id)
+            this_sample, is_dmp_sample = self.get_regular_sample(sample_tumor, "Tumor")
+            if is_dmp_sample:
+                dmp_samples.append(this_sample)
+            sample_normal = dict(
+                pi=pi, bait_set=bait_set, pi_email=pi_email, patient_id=patient_id, sample_id=normal_id
+            )
+            this_sample, is_dmp_sample = self.get_regular_sample(sample_normal, "Normal")
+            if is_dmp_sample:
+                dmp_samples.append(this_sample)
+        return dmp_samples
+
+    def links_to_files(self):
+        jira_id = JobGroupNotifier.objects.get(id=self.job_group_notifier_id).jira_id
+        result = dict()
+        result[
+            "Sample Pairing"
+        ] = f"{settings.DELIVERY_FILE_SERVER}/project/{self.request_id}/jira/{jira_id}/sample_pairing.txt"
+        result[
+            "Sample Mapping"
+        ] = f"{settings.DELIVERY_FILE_SERVER}/project/{self.request_id}/jira/{jira_id}/sample_mapping.txt"
+        result[
+            "Sample Data Clinical"
+        ] = f"{settings.DELIVERY_FILE_SERVER}/project/{self.request_id}/jira/{jira_id}/sample_data_clinical.txt"
+        return result
diff --git a/runner/operator/argos_operator/v2_1_0/bin/make_sample.py b/runner/operator/argos_operator/v2_1_0/bin/make_sample.py
new file mode 100644
index 000000000..ed3b85f51
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/bin/make_sample.py
@@ -0,0 +1,372 @@
+"""
+This constructs a sample dictionary from the metadata in the Voyager/Beagle database
+"""
+import logging
+import os
+import re
+from django.conf import settings
+from runner.operator.helper import format_sample_name, get_r_orientation, spoof_barcode
+from file_system.repository.file_repository import FileRepository
+from pprint import pprint
+
+LOGGER = logging.getLogger(__name__)
+
+
+def remove_with_caveats(samples):
+    """
+    Removes samples from a list of samples if they either
+    don't contain a 'sampleNameMalformed', which happens when function
+    format_sample_name returns it
+    """
+    data = list()
+    error_data = list()
+    for sample in samples:
+        add = True
+        sample_id = sample["sample_id"]
+        sample_name = sample["SM"]
+        patient_id = sample["patient_id"]
+        if sample_name == "sampleNameMalformed":
+            add = False
+            LOGGER.info("Sample name is malformed for for %s; removing from set", sample_id)
+        if not patient_id:
+            add = False
+            LOGGER.info("No patient ID for sample %s; removing from set", sample_id)
+        elif isinstance(patient_id, str):
+            if not patient_id.strip():
+                add = False
+                LOGGER.info("Empty string for patient ID in sample %s; removing from set", sample_id)
+        if add:
+            data.append(sample)
+        else:
+            error_data.append(sample)
+    return data, error_data
+
+
+def check_and_return_single_values(data):
+    """
+    data is a dictionary; each key contains a list of values.
+
+    single_values are the expected keys that should contain only one value
+
+    Concatenating pi and pi_email AND formatting the LB field are workarounds
+    because some samples would have multiple values for these but the sample dict
+    it returns must have one value only in order for the pipeline to execute
+    """
+    single_values = [
+        "CN",
+        "PL",
+        "SM",
+        "bait_set",
+        "patient_id",
+        "species",
+        "tumor_type",
+        "sample_id",
+        "specimen_type",
+        "request_id",
+        "run_mode",
+    ]
+
+    for key in single_values:
+        value = set(data[key])
+        if len(value) == 1:
+            data[key] = value.pop()
+        else:
+            LOGGER.error("Expected only one value for %s!", key)
+            LOGGER.error("Check import, something went wrong.")
+
+    # concatenating pi and pi_email
+    if data["pi"] == [None]:
+        data["pi"] = [""]
+    if data["pi_email"] == [None]:
+        data["pi_email"] = [""]
+    data["pi"] = "; ".join(set(data["pi"]))
+    data["pi_email"] = "; ".join(set(data["pi_email"]))
+
+    # hack; formats LB field so that it is a string
+    library_id = [i for i in data["LB"] if i]
+    number_of_library_ids = len(library_id)
+    if number_of_library_ids > 0:
+        library_id.sort()
+        data["LB"] = "_and_".join(library_id)
+    else:
+        data["LB"] = data["SM"] + "_1"
+
+    # run_ids need to be one list
+    run_ids = data["run_id"]
+    if any(isinstance(run_id, list) for run_id in run_ids):
+        run_ids = [item for sublist in run_ids for item in sublist]
+        data["run_id"] = list(set(run_ids))
+    return data
+
+
+def get_file(fpath):
+    files = FileRepository.all()
+    data = FileRepository.filter(queryset=files, path=fpath)
+    if data:
+        return data[0]
+    return None
+
+
+def get_run_mode(run_mode):
+    if isinstance(run_mode, str):
+        if "hiseq" in run_mode.lower():
+            return "hiseq"
+        if "novaseq" in run_mode.lower():
+            return "novaseq"
+        return run_mode
+
+
+def build_sample(data, ignore_sample_formatting=False):
+    """
+    Given some data - which is a list of samples originally from the LIMS, split up into one file
+    per index - the data is then compiled into one sample dictionary consisting of one or more
+    pairs of fastqs
+
+    Note that ID and SM are different field values in ARGOS (RG_ID and ID, respectively, in ARGOS)
+    but standardizing it here with what GATK sets bam headers to
+    """
+
+    samples = dict()
+
+    for value in data:
+        fpath = value["path"]
+        curr_file = get_file(fpath)
+        meta = value["metadata"]
+        bid = value["id"]
+        sequencing_center = meta["sequencingCenter"]
+        platform = meta["platform"]
+        request_id = meta[settings.REQUEST_ID_METADATA_KEY]
+        sample_id = meta[settings.SAMPLE_ID_METADATA_KEY]
+        library_id = meta[settings.LIBRARY_ID_METADATA_KEY]
+        bait_set = meta["baitSet"]
+        tumor_type = meta["tumorOrNormal"]
+        specimen_type = meta[settings.SAMPLE_CLASS_METADATA_KEY]
+        species = meta["species"]
+        cmo_sample_name = format_sample_name(
+            meta[settings.CMO_SAMPLE_NAME_METADATA_KEY], specimen_type, ignore_sample_formatting
+        )
+        if cmo_sample_name == "sampleNameMalformed":
+            LOGGER.error("sampleName for %s is malformed", sample_id)
+        flowcell_id = meta["flowCellId"]
+        barcode_index = meta["barcodeIndex"]
+        cmo_patient_id = meta[settings.PATIENT_ID_METADATA_KEY]
+        platform_unit = flowcell_id
+        run_date = meta["runDate"]
+        r_orientation = meta["R"]
+        pi_name = meta.get("pi", meta["labHeadName"])
+        pi_email = meta.get("pi_email", meta["labHeadEmail"])
+        run_id = meta["runId"]
+        preservation_type = meta["preservation"]
+        rg_id = cmo_sample_name + "_1"
+        run_mode = get_run_mode(meta["runMode"])
+        if barcode_index:
+            platform_unit = "_".join([flowcell_id, barcode_index])
+        try:
+            rg_id = "_".join([cmo_sample_name, platform_unit])
+        except:
+            LOGGER.info("RG ID couldn't be set.")
+            LOGGER.info("Sample ID %s; patient ID %s", sample_id, cmo_patient_id)
+            LOGGER.info("SampleName %s; platform unit %s", cmo_sample_name, platform_unit)
+        if sample_id not in samples:
+            samples[sample_id] = dict()
+            sample = dict()
+            sample["CN"] = sequencing_center
+            sample["PL"] = platform
+            sample["PU"] = list()
+            sample["LB"] = library_id
+            sample["tumor_type"] = tumor_type
+            sample["SM"] = cmo_sample_name
+            sample["species"] = species
+            sample["patient_id"] = cmo_patient_id
+            sample["bait_set"] = bait_set
+            sample["sample_id"] = sample_id
+            sample["run_date"] = run_date
+            sample["specimen_type"] = specimen_type
+            sample["request_id"] = request_id
+            sample["pi"] = pi_name
+            sample["pi_email"] = pi_email
+            sample["run_id"] = run_id
+            sample["preservation_type"] = preservation_type
+            sample["ID"] = list()
+            sample["R1"] = list()
+            sample["R1_bid"] = list()
+            sample["R2"] = list()
+            sample["R2_bid"] = list()
+            sample["fastqs"] = list()
+            sample["run_mode"] = run_mode
+        else:
+            sample = samples[sample_id]
+
+        # Queueing up fastqs for pairing later; RG ID and PU
+        # will be assigned based on Fastqs object
+        if "R1" in r_orientation or "R2" in r_orientation:
+            sample["fastqs"].append(curr_file)
+        else:
+            # DMP bams found; assigning RG ID and PU here
+            # There will always be only one DMP bam, so assign explicitly
+            sample["bam"] = fpath
+            sample["bam_bid"] = bid
+            sample["PU"] = platform_unit
+            sample["ID"] = rg_id
+        samples[sample_id] = sample
+
+    result = dict()
+    result["CN"] = list()
+    result["PL"] = list()
+    result["PU"] = list()
+    result["LB"] = list()
+    result["tumor_type"] = list()
+    result["ID"] = list()
+    result["SM"] = list()
+    result["species"] = list()
+    result["patient_id"] = list()
+    result["bait_set"] = list()
+    result["sample_id"] = list()
+    result["run_date"] = list()
+    result["specimen_type"] = list()
+    result["R1"] = list()
+    result["R2"] = list()
+    result["R1_bid"] = list()
+    result["R2_bid"] = list()
+    result["bam"] = list()
+    result["bam_bid"] = list()
+    result["request_id"] = list()
+    result["pi"] = list()
+    result["pi_email"] = list()
+    result["run_id"] = list()
+    result["preservation_type"] = list()
+    result["run_mode"] = list()
+
+    for sample_id in samples:
+        sample = samples[sample_id]
+        for key in sample:
+            if key == "fastqs":
+                if sample["fastqs"]:
+                    fastqs = Fastqs(sample["SM"], sample["fastqs"])
+                    result["R1"] = fastqs.r1
+                    result["R1_bid"] = fastqs.r1_bids
+                    result["R2"] = fastqs.r2
+                    result["R2_bid"] = fastqs.r2_bids
+                    result["PU"] = fastqs.pu
+                    result["ID"] = fastqs.rg_id
+            else:
+                result[key].append(sample[key])
+    result = check_and_return_single_values(result)
+    return result
+
+
+class Fastqs:
+    """
+    Fastqs class to hold pairs of fastqs
+
+    Does the pairing from a list of files
+
+    The paired bool is True if all of the R1s in file list find a matching R2
+    """
+
+    def __init__(self, sample_name, file_list):
+        self.sample_name = sample_name
+        self.fastqs = dict()
+        self.r1 = list()
+        self.r2 = list()
+        self.r2_bids = list()
+        self.paired = True
+        self._set_R(file_list)
+        self.r1_bids = self._set_bids(self.r1)
+        self.r2_bids = self._set_bids(self.r2)
+        self.pu = self._set_pu()
+        self.rg_id = self._set_rg_id()
+
+    def _set_bids(self, r):
+        r_bids = list()
+        for f in r:
+            r_file = get_file(f)
+            r_bids.append(r_file.id)
+        return r_bids
+
+    def _set_pu(self):
+        """
+        Creating a list of PU values; used by argos pipeline as scatter input
+
+        Only iterating across r1s since r1 and r2 should have the same metadata
+        """
+        pu = list()
+        for f in self.r1:
+            metadata = get_file(f).metadata
+            flowcell_id = "MT_FCID"
+            if "poolednormal" in self.sample_name.lower():
+                flowcell_id = "PN_FCID"
+                r = get_r_orientation(f)
+                barcode_index = spoof_barcode(os.path.basename(f), r)
+            else:
+                fid = metadata["flowCellId"]
+                if fid:
+                    flowcell_id = fid
+                barcode_index = metadata["barcodeIndex"]
+            platform_unit = flowcell_id
+            if barcode_index:
+                platform_unit = "_".join([flowcell_id, barcode_index])
+            pu.append(platform_unit)
+        return pu
+
+    def _set_rg_id(self):
+        """
+        Creating a list of RG_ID values; used by argos pipeline as scatter input
+
+        Only iterating across r1s since r1 and r2 should have the same metadata
+        """
+        rg_ids = list()
+        for i, f in enumerate(self.r1):
+            metadata = get_file(f).metadata
+            sample_name = self.sample_name
+            pu = self.pu[i]
+            rg_id = "_".join([sample_name, pu])
+            rg_ids.append(rg_id)
+        return rg_ids
+
+    def _set_R(self, file_list):
+        """
+        From the file list, retrieve R1 and R2 fastq files
+
+        Sets PU and bids, as well
+
+        Uses _get_fastq_from_list() to find R2 pair.
+        """
+        r1s = list()
+        r2s = list()
+        for i in file_list:
+            f = i.file
+            r = get_r_orientation(f.path)
+            if r == "R1":
+                r1s.append(f)
+            if r == "R2":
+                r2s.append(f)
+        for f in r1s:
+            self.r1.append(f.path)
+            fastq1 = f.path
+            expected_r2 = "R2".join(fastq1.rsplit("R1", 1))
+            fastq2 = self._get_fastq_from_list(expected_r2, r2s)
+            if fastq2:
+                self.r2.append(fastq2.path)
+            else:
+                print("No fastq R2 found for %s" % f.path)
+                self.paired = False
+
+    def __str__(self):
+        s = "R1:\n"
+        for i in self.r1:
+            s += i.path + "\n"
+        s += "\nR2:\n"
+        for i in self.r2:
+            s += i.path + "\n"
+        return s
+
+    def _get_fastq_from_list(self, fastq_path, fastq_files):
+        """
+        Given fastq_path, find it in the list of fastq_files and return
+        that File object
+        """
+        for f in fastq_files:
+            fpath = f.path
+            if fastq_path == fpath:
+                return f
diff --git a/runner/operator/argos_operator/v2_1_0/bin/pair_request.py b/runner/operator/argos_operator/v2_1_0/bin/pair_request.py
new file mode 100644
index 000000000..c87caa9b2
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/bin/pair_request.py
@@ -0,0 +1,260 @@
+"""
+Given a request, this module will attempt to find a pair for every tumor within that request.
+
+It performs the following:
+
+  - Checks if a normal exists within that same request
+  - Checks if a normal exists in a different request for that same patient
+  - Checks if a DMP bam will be within that request (unimplemented as of 2020-03-03; upcoming)
+  - Checks if a pooled normal exists
+
+Normals will have to have the same patient and bait set in order to be considered "viable"
+"""
+import logging
+from datetime import datetime as dt
+from .retrieve_samples_by_query import get_samples_from_patient_id, get_pooled_normals, get_dmp_bam
+
+LOGGER = logging.getLogger(__name__)
+
+
+def get_by_tumor_type(data, tumor_type):
+    """
+    Retrieves a set of samples that contain a value tumor_type
+
+    tumor_tupe is typically Normal or Tumor
+    """
+    samples = list()
+    for sample in data:
+        if tumor_type.lower() in sample["tumor_type"].lower():
+            samples.append(sample)
+    return samples
+
+
+def compare_dates(normal, viable_normal, date_string):
+    """
+    Compares dates between two normals; returns the most recent
+    """
+    for run_date in normal["run_date"]:
+        normal_date = dt.strptime(run_date, date_string)
+        for vrun_date in viable_normal["run_date"]:
+            vnormal_date = dt.strptime(vrun_date, date_string)
+            if vnormal_date < normal_date:
+                viable_normal = normal
+    return viable_normal
+
+
+def get_viable_normal(normals, patient_id, run_mode, bait_set):
+    """
+    From a set of normals, return the ones that have matching patient_id, bait_set,
+    run_mode, and the most recent
+
+    Does not check for Pooled Normals; that's done separately
+    """
+    viable_normal = dict()
+    for normal in normals:
+        normal_run_mode = get_run_mode(normal["run_mode"])
+        if normal["patient_id"] == patient_id and normal["bait_set"] == bait_set and normal_run_mode == run_mode:
+            if viable_normal:
+                try:
+                    viable_normal = compare_dates(normal, viable_normal, "%y-%m-%d")
+                except ValueError:
+                    LOGGER.debug("Trying different date parser")
+                    viable_normal = compare_dates(normal, viable_normal, "%Y-%m-%d")
+            else:
+                viable_normal = normal
+    return viable_normal
+
+
+def compile_pairs(samples, pairing_info=None, logger=None):
+    """
+    Creates pairs of tumors and normals from a list of samples
+    """
+    tumors = get_by_tumor_type(samples, "Tumor")
+    normals = get_by_tumor_type(samples, "Normal")
+
+    # pairing
+    pairs = dict()
+    pairs["tumor"] = list()
+    pairs["normal"] = list()
+
+    num_tumors = len(tumors)
+    if num_tumors == 0:
+        LOGGER.error("No tumor samples found; pairing will not be performed.")
+        logger.log("No tumor samples found; pairing will not be performed.") if logger else None
+        LOGGER.error("Returning an empty list of pairs.")
+        logger.log("Returning an empty list of pairs.") if logger else None
+
+    for tumor in tumors:
+        LOGGER.info("Pairing tumor sample %s", tumor["sample_id"])
+        logger.log("Returning an empty list of pairs.") if logger else None
+        if pairing_info:
+            """
+            Creating samples, based on predefined pairing list
+            """
+            for pair in pairing_info["pairs"]:
+                tumor_sample = pair["tumor"]
+                if tumor["SM"] == tumor_sample["sample_id"]:
+                    for normal in normals:
+                        normal_sample = pair["normal"]
+                        if "poolednormal" in normal_sample["sample_id"].lower():
+                            """
+                            We should have a pool normal to pair with here
+                            """
+                            normal_run_ids = normal["run_id"]
+                            normal_bait_set = normal["bait_set"]
+                            normal_preservation = [x.lower() for x in normal["preservation_type"]]
+                            if all(elem in normal_run_ids for elem in tumor["run_id"]):
+                                """
+                                Checks if any of the run ids in the pre-created normal samples
+                                are also all in the tumor sample
+                                """
+                                if normal_bait_set.lower() in tumor["bait_set"].lower():
+                                    tumor_preservations = [x.lower() for x in tumor["preservation_type"]]
+                                    # Must check if FFPE or other
+                                    if "ffpe" in normal_preservation:
+                                        if "ffpe" in tumor_preservations:
+                                            pairs["tumor"].append(tumor)
+                                            pairs["normal"].append(normal)
+                                            break
+                                    else:
+                                        if "ffpe" not in tumor_preservations:
+                                            pairs["tumor"].append(tumor)
+                                            pairs["normal"].append(normal)
+                                            break
+                        elif normal["SM"] == normal_sample["sample_id"]:
+                            """
+                            This pairs with a dmp normal
+                            """
+                            pairs["tumor"].append(tumor)
+                            pairs["normal"].append(normal)
+                            break
+        else:
+            patient_id = tumor["patient_id"]
+            if patient_id:
+                run_mode = get_run_mode(tumor["run_mode"])
+                bait_set = tumor["bait_set"]
+                run_ids = tumor["run_id"]
+                preservation_types = tumor["preservation_type"]
+                normal = get_viable_normal(normals, patient_id, run_mode, bait_set)
+                if normal:
+                    LOGGER.info(
+                        "Pairing %s (%s) with %s (%s)",
+                        tumor["sample_id"],
+                        tumor["SM"],
+                        normal["sample_id"],
+                        normal["SM"],
+                    )
+                    logger.log(
+                        f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {normal['sample_id']} ({normal['SM']})"
+                    ) if logger else None
+                    pairs["tumor"].append(tumor)
+                    pairs["normal"].append(normal)
+                else:
+                    LOGGER.info(
+                        "Missing normal for sample %s (%s); querying patient %s",
+                        tumor["sample_id"],
+                        tumor["SM"],
+                        patient_id,
+                    )
+                    logger.log(
+                        f"Missing normal for sample {tumor['sample_id']} ({tumor['SM']}); querying patient {patient_id}"
+                    ) if logger else None
+                    patient_samples = get_samples_from_patient_id(patient_id)
+                    new_normals = get_by_tumor_type(patient_samples, "Normal")
+                    new_normal = get_viable_normal(new_normals, patient_id, run_mode, bait_set)
+                    if new_normal:
+                        LOGGER.info(
+                            "Pairing %s (%s) with %s (%s)",
+                            tumor["sample_id"],
+                            tumor["SM"],
+                            new_normal["sample_id"],
+                            new_normal["SM"],
+                        )
+                        logger.log(
+                            f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {new_normal['sample_id']} ({new_normal['SM']})"
+                        ) if logger else None
+                        pairs["tumor"].append(tumor)
+                        pairs["normal"].append(new_normal)
+                    else:
+                        LOGGER.info("No normal found for patient %s; checking for DMP Normal", patient_id)
+                        logger.log(
+                            f"No normal found for patient {patient_id}; checking for DMP Normal"
+                        ) if logger else None
+                        dmp_normal = get_dmp_bam(patient_id, bait_set, "Normal")
+                        if dmp_normal:
+                            LOGGER.info(
+                                "Pairing %s (%s) with %s (%s)",
+                                tumor["sample_id"],
+                                tumor["SM"],
+                                dmp_normal["sample_id"],
+                                dmp_normal["SM"],
+                            )
+                            logger.log(
+                                f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {dmp_normal['sample_id']} ({dmp_normal['SM']})"
+                            ) if logger else None
+                            pairs["tumor"].append(tumor)
+                            pairs["normal"].append(dmp_normal)
+                        else:
+                            pooled_normal = get_pooled_normals(run_ids, preservation_types, bait_set)
+                            LOGGER.info("No DMP Normal found for patient %s; checking for Pooled Normal", patient_id)
+                            logger.log(
+                                f"No DMP Normal found for patient %s; checking for Pooled Normal {patient_id}"
+                            ) if logger else None
+                            if pooled_normal:
+                                LOGGER.info(
+                                    "Pairing %s (%s) with %s (%s)",
+                                    tumor["sample_id"],
+                                    tumor["SM"],
+                                    pooled_normal["sample_id"],
+                                    pooled_normal["SM"],
+                                )
+                                logger.log(
+                                    f"Pairing {tumor['sample_id']} ({tumor['SM']}) with {pooled_normal['sample_id']} ({pooled_normal['SM']})"
+                                ) if logger else None
+                                pairs["tumor"].append(tumor)
+                                pairs["normal"].append(pooled_normal)
+                            else:
+                                LOGGER.error(
+                                    "No normal found for %s (%s), patient %s",
+                                    tumor["sample_id"],
+                                    tumor["SM"],
+                                    patient_id,
+                                )
+                                logger.log(
+                                    f"No normal found for {tumor['sample_id']} ({tumor['SM']}), patient {patient_id}"
+                                ) if logger else None
+            else:
+                LOGGER.error(
+                    "NoPatientIdError: No patient_id found for %s (%s); skipping.", tumor["sample_id"], tumor["SM"]
+                )
+                logger.log(
+                    f"NoPatientIdError: No patient_id found for {tumor['sample_id']} ({tumor['SM']}); skipping."
+                ) if logger else None
+
+    return pairs
+
+
+def get_run_mode(run_mode):
+    """
+    Normalizing hiseq and novaseq strings
+    """
+    if run_mode:
+        if "hiseq" in run_mode.lower():
+            return "hiseq"
+        if "novaseq" in run_mode.lower():
+            return "novaseq"
+    return run_mode
+
+
+def create_pairing_info(pairs):
+    """
+    Outputs pairing data in the form of TUMOR\tNORMAL
+    Used in legacy Tempo and Roslin
+    """
+    output_string = ""
+    num_tumors = len(pairs["tumor"])
+    for i in range(0, num_tumors):
+        tumor_name = pairs["tumor"][i]["SM"]
+        normal_name = pairs["normal"][i]["SM"]
+        output_string += "\t".join([normal_name, tumor_name]) + "\n"
+    return output_string
diff --git a/runner/operator/argos_operator/v2_1_0/bin/retrieve_samples_by_query.py b/runner/operator/argos_operator/v2_1_0/bin/retrieve_samples_by_query.py
new file mode 100644
index 000000000..711f05a9c
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/bin/retrieve_samples_by_query.py
@@ -0,0 +1,357 @@
+"""
+Functions for retrieving samples from the database based on other criteria
+that wasn't in the operator
+
+For example, get all samples by a patient ID or pooled normals based on the bait set
+and preservation type
+"""
+import logging
+from file_system.models import MachineRunMode
+from file_system.models import FileGroup
+from file_system.repository.file_repository import FileRepository
+from django.db.models import Q
+from django.conf import settings
+from .make_sample import build_sample, remove_with_caveats, format_sample_name
+from runner.operator.helper import get_r_orientation, spoof_barcode, init_metadata
+
+LOGGER = logging.getLogger(__name__)
+
+
+def build_argos_file_groups_query():
+    ARGOS_FG_SLUGS = ["lims", "origin-unknown"]
+    slug_set = [Q(file__file_group=FileGroup.objects.get(slug=value)) for value in set(ARGOS_FG_SLUGS)]
+    query = slug_set.pop()
+    for item in slug_set:
+        query |= item
+    return query
+
+
+def get_samples_from_patient_id(patient_id):
+    """
+    Retrieves samples from the database based on the patient_id
+
+    Only retrieve patients from LIMS file group
+    """
+    all_files = FileRepository.all()
+    q_pid = Q(("metadata__{}".format(settings.PATIENT_ID_METADATA_KEY), patient_id))
+    q_fg = build_argos_file_groups_query()
+    q = q_pid & q_fg
+    files = FileRepository.filter(queryset=all_files, q=q)
+    files = FileRepository.filter(queryset=files, filter_redact=True)
+    data = list()
+    for current_file in files:
+        sample = dict()
+        sample["id"] = current_file.file.id
+        sample["path"] = current_file.file.path
+        sample["file_name"] = current_file.file.file_name
+        sample["metadata"] = current_file.metadata
+        data.append(sample)
+
+    samples = list()
+    # group by igoId
+    igo_id_group = dict()
+    for sample in data:
+        igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+        if igo_id not in igo_id_group:
+            igo_id_group[igo_id] = list()
+        igo_id_group[igo_id].append(sample)
+
+    for igo_id in igo_id_group:
+        samples.append(build_sample(igo_id_group[igo_id]))
+    samples, bad_samples = remove_with_caveats(samples)
+    number_of_bad_samples = len(bad_samples)
+    if number_of_bad_samples > 0:
+        LOGGER.warning("Some samples for patient query %s have invalid %i values", patient_id, number_of_bad_samples)
+    return samples
+
+
+def get_descriptor(bait_set, pooled_normals, preservation_types, run_ids):
+    """
+    Need descriptor to match pooled normal "genePanel", which might need to be re-labeled as bait_set
+
+    Adding correction for IMPACT505 pooled normals
+    """
+    query = Q(file__file_group=settings.POOLED_NORMAL_FILE_GROUP)
+    sample_name = None
+
+    descriptor = None
+    for pooled_normal in pooled_normals:
+        bset_data = pooled_normal.metadata[settings.RECIPE_METADATA_KEY]
+        if bset_data.lower() in bait_set.lower():
+            descriptor = bset_data
+
+    if descriptor:  # From returned pooled normals, we found the bait set/genePanel we're looking for
+        pooled_normals = FileRepository.filter(
+            queryset=pooled_normals, metadata={settings.RECIPE_METADATA_KEY: descriptor}
+        )
+
+        # sample_name is FROZENPOOLEDNORMAL unless FFPE is in any of the preservation types
+        # in preservation_types
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        run_ids_suffix_list = [i for i in run_ids if i]  # remove empty or false string values
+        run_ids_suffix = "_".join(set(run_ids_suffix_list))
+        sample_name = "FROZENPOOLEDNORMAL_" + run_ids_suffix
+        if "ffpe" in preservations_lower_case:
+            sample_name = "FFPEPOOLEDNORMAL_" + run_ids_suffix
+    elif "impact505" in bait_set.lower():
+        # We didn't find a pooled normal for IMPACT505; return "static" FROZEN or FFPE pool normal
+        descriptor = "IMPACT505"
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        machine = get_sequencer_type(run_ids)
+        if not machine:
+            LOGGER.error("Could not find IMPACT505 pooled normal for $s; new machine name?", sample_name)
+        if machine == "hiseq":
+            sample_name = "FROZENPOOLEDNORMAL_IMPACT505_V1"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_IMPACT505_V1"
+        if machine == "novaseq":
+            sample_name = "FROZENPOOLEDNORMAL_IMPACT505_V2"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_IMPACT505_V2"
+        q = query & Q(("metadata__{}".format(settings.SAMPLE_NAME_METADATA_KEY), sample_name))
+        pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+        if not pooled_normals:
+            LOGGER.error("Could not find IMPACT505 pooled normal to pair %s", sample_name)
+    elif "hemepact_v4" in bait_set.lower():
+        # We didn't find a pooled normal for HemePACT_v4; return "static" FROZEN or FFPE pool normal
+        descriptor = "HemePACT_v4"
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        machine = get_sequencer_type(run_ids)
+        if not machine:
+            LOGGER.error("Could not find HemePACT_v4 pooled normal for $s; new machine name?", sample_name)
+        if machine == "hiseq":
+            sample_name = "FROZENPOOLEDNORMAL_HemePACT_v4_V1"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_HemePACT_v4_V1"
+        if machine == "novaseq":
+            sample_name = "FROZENPOOLEDNORMAL_HemePACT_v4_V2"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_HemePACT_v4_V2"
+        q = query & Q(("metadata__{}".format(settings.SAMPLE_NAME_METADATA_KEY), sample_name))
+        pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+        if not pooled_normals:
+            LOGGER.error("Could not find HemePACT_v4 pooled normal to pair %s", sample_name)
+    elif "impact-heme_v2" in bait_set.lower():
+        # We didn't find a pooled normal for IMPACT-Heme_v2; return "static" FROZEN or FFPE pool normal
+        descriptor = "IMPACT-Heme_v2"
+        preservations_lower_case = set([x.lower() for x in preservation_types])
+        machine = get_sequencer_type(run_ids)
+        if not machine:
+            LOGGER.error("Could not find IMPACT-Heme_v2 pooled normal for $s; new machine name?", sample_name)
+        if machine == "novaseq":
+            sample_name = "FROZENPOOLEDNORMAL_IMPACT-Heme_v2_V1"
+            if "ffpe" in preservations_lower_case:
+                sample_name = "FFPEPOOLEDNORMAL_IMPACT-Heme_v2_V1"
+        q = query & Q(("metadata__{}".format(settings.SAMPLE_NAME_METADATA_KEY), sample_name))
+        pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+        if not pooled_normals:
+            LOGGER.error("Could not find IMPACT-Heme_v2 pooled normal to pair %s", sample_name)
+
+    return pooled_normals, descriptor, sample_name
+
+
+def get_sequencer_type(run_ids_list):
+    run_ids_lower = [i.lower() for i in run_ids_list if i]
+    machine_modes = MachineRunMode.objects.all()
+    for machine in machine_modes:
+        if find_substr(machine.machine_name, run_ids_lower):
+            return machine.machine_class
+
+
+def find_substr(s, l):
+    return any(s in string for string in l)
+
+
+def build_run_id_query(data):
+    """
+    Build complex Q object run id query from given data
+
+    Only does OR queries, as seen in line
+
+       query |= item
+
+    Very similar to build_preservation_query, but "filemetadata__metadata__runId"
+    can't be sent as a value, so had to make a semi-redundant function
+    """
+    data_query_set = [Q(metadata__runId=value) for value in set(data)]
+    query = data_query_set.pop()
+    for item in data_query_set:
+        query |= item
+    return query
+
+
+def build_preservation_query(data):
+    """
+    Build simple query for either FROZEN or FFPE pooled normal
+
+    Main logic: if FFPE in data, return FFPE query; else, return FROZEN query
+    """
+    preservations_lower_case = set([x.lower() for x in data])
+    value = "FROZEN"
+    if "ffpe" in preservations_lower_case:
+        value = "FFPE"
+    # case-insensitive matching
+    query = Q(metadata__preservation__iexact=value)
+    return query
+
+
+def get_pooled_normals(run_ids, preservation_types, bait_set):
+    """
+    From a list of run_ids, preservation types, and bait sets, get all potential pooled normals
+    """
+    pooled_normals, descriptor, sample_name = get_pooled_normal_files(run_ids, preservation_types, bait_set)
+    sample_files = list()
+    for pooled_normal_file in pooled_normals:
+        sample_file = build_pooled_normal_sample_by_file(
+            pooled_normal_file, run_ids, preservation_types, descriptor, sample_name
+        )
+        sample_files.append(sample_file)
+    pooled_normal = build_sample(sample_files, ignore_sample_formatting=True)
+
+    return pooled_normal
+
+
+def get_pooled_normal_files(run_ids, preservation_types, bait_set):
+
+    pooled_normals = FileRepository.all()
+
+    query = Q(file__file_group=settings.POOLED_NORMAL_FILE_GROUP)
+    run_id_query = build_run_id_query(run_ids)
+    preservation_query = build_preservation_query(preservation_types)
+
+    q = query & run_id_query & preservation_query
+
+    pooled_normals = FileRepository.filter(queryset=pooled_normals, q=q)
+
+    pooled_normals, descriptor, sample_name = get_descriptor(bait_set, pooled_normals, preservation_types, run_ids)
+
+    return pooled_normals, descriptor, sample_name
+
+
+def build_pooled_normal_sample_by_file(pooled_normal, run_ids, preservation_types, bait_set, sample_name):
+    specimen_type = "Pooled Normal"
+    sample = dict()
+    sample["id"] = pooled_normal.file.id
+    sample["path"] = pooled_normal.file.path
+    sample["file_name"] = pooled_normal.file.file_name
+    metadata = init_metadata()
+    metadata[settings.SAMPLE_ID_METADATA_KEY] = sample_name
+    metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY] = sample_name
+    metadata[settings.CMO_SAMPLE_TAG_METADATA_KEY] = sample_name
+    metadata[settings.REQUEST_ID_METADATA_KEY] = sample_name
+    metadata["sequencingCenter"] = "MSKCC"
+    metadata["platform"] = "Illumina"
+    metadata["baitSet"] = bait_set
+    metadata[settings.RECIPE_METADATA_KEY] = bait_set
+    metadata["runId"] = run_ids
+    metadata["preservation"] = preservation_types
+    metadata[settings.LIBRARY_ID_METADATA_KEY] = sample_name + "_1"
+    # because rgid depends on flowCellId and barcodeIndex, we will
+    # spoof barcodeIndex so that pairing can work properly; see
+    # build_sample in runner.operator.argos_operator.bin
+    metadata["R"] = get_r_orientation(pooled_normal.file.file_name)
+    metadata["barcodeIndex"] = spoof_barcode(sample["file_name"], metadata["R"])
+    metadata["flowCellId"] = "PN_FCID"
+    metadata["tumorOrNormal"] = "Normal"
+    metadata[settings.PATIENT_ID_METADATA_KEY] = "PN_PATIENT_ID"
+    metadata[settings.SAMPLE_CLASS_METADATA_KEY] = specimen_type
+    metadata["runMode"] = ""
+    metadata[settings.CMO_SAMPLE_CLASS_METADATA_KEY] = ""
+    sample["metadata"] = metadata
+    return sample
+
+
+def get_dmp_bam(patient_id, bait_set, tumor_type):
+    """
+    From a patient id and bait set, get matching dmp bam normal
+    """
+    file_objs = FileRepository.all()
+
+    dmp_query = build_dmp_query(patient_id, bait_set)
+
+    dmp_bam = FileRepository.filter(queryset=file_objs, q=dmp_query).order_by("file__file_name").first()
+
+    if dmp_bam:
+        sample = build_dmp_sample(dmp_bam, patient_id, bait_set, tumor_type)
+        built_sample = build_sample([sample], ignore_sample_formatting=True)
+        return built_sample
+    return None
+
+
+def build_dmp_sample(dmp_bam, patient_id, bait_set, tumor_type, request_id=None, pi=None, pi_email=None):
+
+    dmp_metadata = dmp_bam.metadata
+    specimen_type = "DMP"
+    sample_name = dmp_metadata["external_id"]
+    sequencingCenter = "MSKCC"
+    platform = "Illumina"
+    sample = dict()
+    sample["id"] = dmp_bam.file.id
+    sample["path"] = dmp_bam.file.path
+    sample["file_name"] = dmp_bam.file.file_name
+    sample["file_type"] = dmp_bam.file.file_type
+    metadata = init_metadata()
+    metadata[settings.SAMPLE_ID_METADATA_KEY] = format_sample_name(sample_name, specimen_type)
+    metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY] = format_sample_name(sample_name, specimen_type)
+    metadata[settings.CMO_SAMPLE_TAG_METADATA_KEY] = metadata[settings.CMO_SAMPLE_NAME_METADATA_KEY]
+    metadata[settings.REQUEST_ID_METADATA_KEY] = request_id
+    metadata["request_id"] = request_id
+    metadata["investigatorSampleId"] = dmp_metadata["sample"]
+    metadata["sequencingCenter"] = sequencingCenter
+    metadata["platform"] = platform
+    metadata["baitSet"] = bait_set
+    metadata[settings.RECIPE_METADATA_KEY] = bait_set
+    metadata["run_id"] = ""
+    metadata["preservation"] = ""
+    metadata[settings.LIBRARY_ID_METADATA_KEY] = sample_name + "_1"
+    metadata["R"] = "Not applicable"
+    # because rgid depends on flowCellId and barcodeIndex, we will
+    # spoof barcodeIndex so that pairing can work properly; see
+    # build_sample in runner.operator.argos_operator.bin
+    metadata["barcodeIndex"] = "DMP_BARCODEIDX"
+    metadata["flowCellId"] = "DMP_FCID"
+    metadata["tumorOrNormal"] = tumor_type
+    metadata["sampleType"] = tumor_type
+    metadata[settings.PATIENT_ID_METADATA_KEY] = patient_id
+    metadata[settings.SAMPLE_CLASS_METADATA_KEY] = specimen_type
+    metadata["runMode"] = ""
+    metadata["sex"] = ""
+    metadata["tissueLocation"] = dmp_metadata.get("tissue_type", "")
+    metadata[settings.ONCOTREE_METADATA_KEY] = dmp_metadata.get("tumor_type", "")
+    metadata[settings.CMO_SAMPLE_CLASS_METADATA_KEY] = ""
+    if pi:
+        metadata["pi"] = pi
+    if pi_email:
+        metadata["pi_email"] = pi_email
+    sample["metadata"] = metadata
+    return sample
+
+
+def build_dmp_query(patient_id, bait_set):
+    """
+    Build simple Q queries for patient id, bait set, and type 'N' to signify "normal"
+
+    The bait set from file groups/LIMS is different from what's in DMP, so this
+    translates it.
+
+    Patient ID in DMP also doesn't contain C-, so this removes that prefix
+    """
+    value = ""
+    if "impact341" in bait_set.lower():
+        value = "IMPACT341"
+    if "impact410" in bait_set.lower():
+        value = "IMPACT410"
+    if "impact468" in bait_set.lower():
+        value = "IMPACT468"
+    if "hemepact_v4" in bait_set.lower():
+        value = "HEMEPACT"
+    if "impact505" in bait_set.lower():
+        value = "IMPACT505"
+    assay = Q(metadata__cmo_assay=value)
+    # formatting to look like CMO patient IDs in dmp2cmo
+    if "C-" in patient_id[:2]:
+        patient_id = patient_id[2:]
+    patient = Q(metadata__patient__cmo=patient_id)
+    normal = Q(metadata__type="N")
+    query = assay & patient & normal
+    return query
diff --git a/runner/operator/argos_operator/v2_1_0/construct_argos_pair.py b/runner/operator/argos_operator/v2_1_0/construct_argos_pair.py
new file mode 100644
index 000000000..ec09969bb
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/construct_argos_pair.py
@@ -0,0 +1,299 @@
+import os, sys
+import re
+import json
+import logging
+from pprint import pprint
+from django.conf import settings
+from .bin.make_sample import remove_with_caveats
+from .bin.pair_request import compile_pairs
+from file_system.repository.file_repository import FileRepository
+
+WORKDIR = os.path.dirname(os.path.abspath(__file__))
+LOGGER = logging.getLogger(__name__)
+
+
+PDX_SPECIMEN_TYPES = ["pdx", "xenograft", "xenograftderivedcellline"]
+NON_PDX_SPECIMEN_TYPES = [
+    "biopsy",
+    "blood",
+    "cellLine",
+    "cfdna",
+    "fingernails",
+    "nonpdx",
+    "normal",
+    "organoid",
+    "other",
+    "rapidautopsy",
+    "resection",
+    "saliva",
+    "tumor",
+    "poolednormal",
+    "dmp",
+]
+
+# TODO: generalize
+def load_references():
+    d = json.load(open(os.path.join(WORKDIR, "reference_jsons/genomic_resources.json"), "rb"))
+    return d
+
+
+# TODO: obsolete this once Argos has been in production for a while
+def calculate_abra_ram_size(grouping_dict):
+    return
+
+
+def normalize_igo_text_field(igo_text):
+    # Flatten text data from the Genomics Core
+    # to allow robust exact text matching.
+    #
+    # Allow variance in case and ignore non
+    # alphanumeric characters (FYI).
+    # Convert to lowercase
+    s = igo_text.lower()
+    # Remove special characters and extra spaces
+    s = re.sub(r"[^a-z0-9]+", "", s)
+    return s
+
+
+# TODO: This is ARGOS-formatted, note the confusing IDs
+def format_sample(data, specimen_type):
+    sample = dict()
+    sample["ID"] = data["SM"]  # TODO: change someday
+    sample["CN"] = data["CN"]
+    sample["LB"] = data["LB"]
+    sample["PL"] = data["PL"]
+    sample["PU"] = data["PU"]
+    sample["R1"] = list()
+    sample["R2"] = list()
+    sample["zR1"] = list()  # TODO: Add for Xenografts
+    sample["zR2"] = list()  # TODO: Add for Xenografts
+    sample["bam"] = list()
+    sample["RG_ID"] = data["ID"]
+    sample["adapter"] = "AGATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGCATCTCGTATGCCGTCTTCTGCTTG"
+    sample["adapter2"] = "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT"
+    sample["bwa_output"] = sample["ID"] + ".bam"
+    sample["request_id"] = data["request_id"]
+    sample["specimen_type"] = data["specimen_type"]
+
+    if specimen_type in PDX_SPECIMEN_TYPES:
+        r1 = "zR1"
+        r2 = "zR2"
+    elif specimen_type in NON_PDX_SPECIMEN_TYPES:
+        r1 = "R1"
+        r2 = "R2"
+    else:
+        raise Exception(f"Invalid Specimen Type: {specimen_type}")
+
+    for i in data["R1"]:
+        if i:
+            sample[r1].append({"class": "File", "location": "juno://" + i})
+    for i in data["R2"]:
+        if i:
+            sample[r2].append({"class": "File", "location": "juno://" + i})
+    for i in data["bam"]:
+        if i:
+            sample["bam"].append({"class": "File", "location": "juno://" + i})
+    return sample
+
+
+def construct_argos_jobs(samples, pairs=None, logger=None):
+    samples, error_samples = remove_with_caveats(samples)
+    pairs = compile_pairs(samples, pairs, logger=logger)
+    number_of_tumors = len(pairs["tumor"])
+    argos_jobs = list()
+    for i in range(0, number_of_tumors):
+        tumor = pairs["tumor"][i]
+        normal = pairs["normal"][i]
+        project_id = tumor["request_id"]
+        assay = tumor["bait_set"]
+        patient_id = tumor["patient_id"]
+        pi = tumor["pi"]
+        pi_email = tumor["pi_email"]
+        job = dict()
+        tumor_specimen_type = normalize_igo_text_field(pairs["tumor"][i]["specimen_type"])
+        normal_sample = format_sample(normal, tumor_specimen_type)
+        tumor_sample = format_sample(tumor, tumor_specimen_type)
+        job["tumor"] = tumor_sample
+        job["normal"] = normal_sample
+        job["assay"] = assay
+        job["pi"] = pi
+        job["pi_email"] = pi_email
+        job["patient_id"] = patient_id
+        references = convert_references(project_id, assay, pi, pi_email)
+        job.update(references)
+        argos_jobs.append(job)
+    return argos_jobs, error_samples
+
+
+def get_curated_bams(assay, request_files):
+    # Default to AgilentExon_51MB_b37_v3 BAMs for all assays except those specified below
+    json_curated_bams = request_files["curated_bams"]["AgilentExon_51MB_b37_v3"]
+
+    if assay.find("IMPACT-Heme") > -1:
+        json_curated_bams = request_files["curated_bams"]["IMPACT-Heme_v2_BAITS"]
+    # Default to IMPACT468_b37 BAMs for all IMPACT/HemePACT assays
+    elif assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        json_curated_bams = request_files["curated_bams"]["IMPACT468_b37"]
+    elif assay.find("IDT_Exome_v1_FP") > -1:
+        json_curated_bams = request_files["curated_bams"]["IDT_Exome_v1_FP_b37"]
+    array = []
+    for bam in json_curated_bams:
+        array.append({"class": "File", "location": str(bam)})
+    return array
+
+
+def get_baits_and_targets(assay, genomic_resources):
+    # probably need similar rules for whatever "Exome" string is in rquest
+    targets = genomic_resources["targets"]
+
+    target_assay = assay
+
+    if assay.find("HemePACT_v4") > -1:
+        target_assay = "HemePACT_v4_BAITS"
+
+    if assay.find("IMPACT-Heme_v2") > -1:
+        target_assay = "IMPACT-Heme_v2_BAITS"
+
+    if assay.find("IMPACT505") > -1:
+        target_assay = "IMPACT505_b37"
+    if assay.find("IMPACT410") > -1:
+        target_assay = "IMPACT410_b37"
+    if assay.find("IMPACT468") > -1:
+        target_assay = "IMPACT468_b37"
+    if assay.find("IMPACT341") > -1:
+        target_assay = "IMPACT341_b37"
+    if assay.find("IDT_Exome_v1_FP") > -1:
+        target_assay = "IDT_Exome_v1_FP_b37"
+    if assay.find("IMPACT468+08390") > -1:
+        target_assay = "IMPACT468_08390"
+    if assay.find("IMPACT468+Poirier_RB1_intron_V2") > -1:
+        target_assay = "IMPACT468_08050"
+
+    if target_assay in targets:
+        return {
+            "bait_intervals": {"class": "File", "location": str(targets[target_assay]["baits_list"])},
+            "target_intervals": {"class": "File", "location": str(targets[target_assay]["targets_list"])},
+            "fp_intervals": {"class": "File", "location": str(targets[target_assay]["FP_intervals"])},
+            "fp_genotypes": {"class": "File", "location": str(targets[target_assay]["FP_genotypes"])},
+        }
+    else:
+        LOGGER.error("ERROR: Targets for Assay not found in genomic_resources.json: %s", assay)
+
+
+def get_facets_cval(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 50
+    return 100
+
+
+def get_facets_pcval(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 100
+    return 500
+
+
+def get_complex_nn(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 0.2
+    return 0.1
+
+
+def get_complex_tn(assay):
+    if assay.find("IMPACT") > -1 or assay.find("HemePACT") > -1:
+        return 0.5
+    return 0.2
+
+
+def convert_references(project_id, assay, pi, pi_email):
+    genomic_resources = load_references()
+    request_files = genomic_resources["request_files"]
+    intervals = get_baits_and_targets(assay, genomic_resources)
+    curated_bams = get_curated_bams(assay, request_files)
+    covariates = ["CycleCovariate", "ContextCovariate", "ReadGroupCovariate", "QualityScoreCovariate"]
+    rf = ["BadCigar"]
+    genome = "GRCh37"
+    delly_type = ["DUP", "DEL", "INV", "INS", "BND"]
+    facets_cval = get_facets_cval(assay)
+    facets_pcval = get_facets_pcval(assay)
+    complex_nn = get_complex_nn(assay)
+    complex_tn = get_complex_tn(assay)
+    temp_dir = "/scratch"
+    if "TMPDIR" in os.environ:
+        if os.environ["TMPDIR"]:
+            temp_dir = os.environ["TMPDIR"]
+
+    files = {
+        "refseq": {"class": "File", "location": str(request_files["refseq"])},
+        "vep_data": str(request_files["vep_data"]),
+        "hotspot_list": str(request_files["hotspot_list"]),
+        "hotspot_list_maf": {"class": "File", "location": str(request_files["hotspot_list_maf"])},
+        "delly_exclude": {"class": "File", "location": str(genomic_resources["genomes"][genome]["delly"])},
+        "hotspot_vcf": str(request_files["hotspot_vcf"]),
+        "facets_snps": {"class": "File", "location": str(genomic_resources["genomes"][genome]["facets_snps"])},
+        "custom_enst": str(request_files["custom_enst"]),
+        "vep_path": str(request_files["vep_path"]),
+        "conpair_markers": str(request_files["conpair_markers"]),
+        "conpair_markers_bed": str(request_files["conpair_markers_bed"]),
+    }
+
+    files.update(intervals)
+
+    out_dict = {
+        "curated_bams": curated_bams,
+        "hapmap": {"class": "File", "location": str(request_files["hapmap"])},
+        "dbsnp": {"class": "File", "location": str(request_files["dbsnp"])},
+        "indels_1000g": {"class": "File", "location": str(request_files["indels_1000g"])},
+        "snps_1000g": {"class": "File", "location": str(request_files["snps_1000g"])},
+        "cosmic": {"class": "File", "location": str(request_files["cosmic"])},
+        "exac_filter": {"class": "File", "location": str(request_files["exac_filter"])},
+        "ref_fasta": {"class": "File", "location": str(request_files["ref_fasta"])},
+        "mouse_fasta": {"class": "File", "location": str(request_files["mouse_fasta"])},
+        "db_files": files,
+    }
+    # emit_original_quals boolean could be problematic; test
+    params = {
+        "abra_scratch": temp_dir,
+        "abra_ram_min": 84000,
+        "genome": genome,
+        "intervals": genomic_resources["genomes"][genome]["intervals"],
+        "mutect_dcov": 50000,
+        "mutect_rf": rf,
+        "num_cpu_threads_per_data_thread": 6,
+        "covariates": covariates,
+        "emit_original_quals": True,
+        "num_threads": 10,
+        "assay": assay,
+        "tmp_dir": temp_dir,
+        "project_prefix": project_id,
+        "opt_dup_pix_dist": "2500",
+        "delly_type": delly_type,
+        "facets_cval": facets_cval,
+        "facets_pcval": facets_pcval,
+        "complex_nn": complex_nn,
+        "complex_tn": complex_tn,
+        "scripts_bin": "/usr/bin",
+        "gatk_jar_path": "/usr/bin/gatk.jar",
+        "pi": pi,
+        "pi_email": pi_email,
+    }
+    out_dict.update({"runparams": params})
+    return out_dict
+
+
+def get_project_prefix(request_id):
+    project_prefix = set()
+    tumors = FileRepository.filter(
+        metadata={settings.REQUEST_ID_METADATA_KEY: request_id, "tumorOrNormal": "Tumor"},
+        filter_redact=True,
+        values_metadata=settings.REQUEST_ID_METADATA_KEY,
+    )
+    for tumor in tumors:
+        project_prefix.add(tumor)
+    return "_".join(sorted(project_prefix))
+
+
+if __name__ == "__main__":
+    request_id = sys.argv[1]
+
+    argos_jobs = construct_argos_jobs(request_id)
+    pprint(argos_jobs)
diff --git a/runner/operator/argos_operator/v2_1_0/reference_jsons/genomic_resources.json b/runner/operator/argos_operator/v2_1_0/reference_jsons/genomic_resources.json
new file mode 100644
index 000000000..febd20d77
--- /dev/null
+++ b/runner/operator/argos_operator/v2_1_0/reference_jsons/genomic_resources.json
@@ -0,0 +1,328 @@
+{
+    "genomes": {
+        "GRCh37": {
+            "bedtools_genome": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/bedtools_genome/human.b37.genome",
+            "bwa_fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta",
+            "cosmic": {
+                "67": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/cosmic/67/CosmicCodingMuts_v67_b37_20131024__NDS.vcf",
+                "74": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/cosmic/74/CosmicCodingMuts_v74_b37_20151120.vcf"
+            },
+            "dbsnp": {
+                "129": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/dbsnp/129/dbsnp_138.b37.excluding_sites_after_129.vcf",
+                "138": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/dbsnp/138/dbsnp_138.b37.vcf"
+            },
+            "delly": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/delly/human.hg19.excl.tsv",
+            "exac": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/exac/ExAC_nonTCGA.r0.3.1.sites.vep.vcf.gz",
+            "facets_snps": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/facets_snps/dbsnp_137.b37__RmDupsClean__plusPseudo50__DROP_SORT.vcf.gz",
+            "fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta",
+            "gencode": {
+                "19": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/gencode/19/gencode.v19.annotation.gtf",
+                "default": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/gencode/19/gencode.v19.annotation.gtf"
+            },
+            "hapmap33": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/hapmap33/hapmap_3.3.b37.vcf",
+            "impact410_targets": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/impact410_targets/IMPACT410_b37_targets.bed",
+            "intervals" : ["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","X","Y","MT"],
+            "indels_1000g": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/indels_1000g/Mills_and_1000G_gold_standard.indels.b37.vcf",
+            "kallisto_index": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/kallisto_index/gencode.v19",
+            "omni": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/omni/1000G_omni2.5.b37.vcf",
+            "snps_1000g": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/snps_1000g/1000G_phase1.snps.high_confidence.b37.vcf",
+            "star_index": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/star_index/withoutJunctions",
+            "vep_cache": "/opt/common/CentOS_6-dev/vep/cache"
+        }
+    },
+    "targets": {
+        "AgilentExon_51MB_b37_v3": {
+            "FP_genotypes": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_FP_tiling_genotypes.txt",
+            "FP_intervals": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_FP_tiling_intervals.intervals",
+            "baits_bed": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_baits.bed",
+            "baits_ilist": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_baits.ilist",
+            "baits_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_baits.intervals",
+            "targets_bed": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_targets.bed",
+            "targets_ilist": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_targets.ilist",
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_targets.intervals",
+            "targets_plus5bp_ilist": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_targets_plus5bp.ilist"
+        },
+        "IDT_Exome_v1_FP_b37": {
+            "FP_genotypes": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/FP_tiling_genotypes.txt",
+            "FP_intervals": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/FP_tiling_intervals.intervals",
+            "baits_bed": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/IDT_Exome_v1_FP_b37_baits.bed",
+            "baits_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/IDT_Exome_v1_FP_b37_baits.ilist",
+            "targets_bed": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/IDT_Exome_v1_FP_b37_targets.bed",
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/IDT_Exome_v1_FP_b37_targets.ilist"
+        },
+        "E90_NimbleGeneV3_WES": {
+            "FP_genotypes": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/E90_NimbleGeneV3_WES/b37/E90_NimbleGeneV3_WES_FP_tiling_genotypes.txt",
+            "FP_intervals": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/E90_NimbleGeneV3_WES/b37/E90_NimbleGeneV3_WES_FP_tiling_intervals.intervals",
+            "baits_bed": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/E90_NimbleGeneV3_WES/b37/E90_NimbleGeneV3_WES_b37_baits.bed",
+            "baits_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/E90_NimbleGeneV3_WES/b37/E90_NimbleGeneV3_WES_b37_baits.ilist",
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+            ],
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+            ],
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+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_HNL3MK_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_K5TJ3T_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_MWEAE4_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_PY8UJ4_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_T4XAKT_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_TLV4D4_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_ULPUE1_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_V16J6D_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_WPM78R_T001_d02.rg.md.abra.printreads.bam",
+                "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/curated_bams/IMPACT-Heme_v2_BAITS/s_C_XN0L0N_T001_d02.rg.md.abra.printreads.bam"
+            ]
+        },
+        "hotspot_list": "/usr/bin/ngs-filters/data/hotspot-list-union-v1-v2.txt",
+        "hotspot_list_maf": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/qc_resources/hotspot-list-union-v1-v2.maf",
+        "hotspot_vcf": "/usr/bin/basicfiltering/data/hotspot-list-union-v1-v2.vcf",
+        "custom_enst": "/usr/bin/vcf2maf/data/isoform_overrides_at_mskcc",
+        "vep_path": "/usr/bin/vep",
+        "ref_fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37/fasta/b37.fasta",
+        "mouse_fasta": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCm38/GRCm38.fasta"
+    }
+}
\ No newline at end of file
diff --git a/runner/operator/argos_report/v1_2_0/argos_report_operator.py b/runner/operator/argos_report/v1_2_0/argos_report_operator.py
new file mode 100644
index 000000000..a9d6e9b1b
--- /dev/null
+++ b/runner/operator/argos_report/v1_2_0/argos_report_operator.py
@@ -0,0 +1,148 @@
+import os
+import logging
+from datetime import datetime
+from django.conf import settings
+from beagle import __version__
+from datetime import datetime
+from file_system.models import File, FileGroup, FileType, FileMetadata
+from file_system.repository.file_repository import FileRepository
+from runner.operator.operator import Operator
+from runner.models import Run, Pipeline
+from runner.run.objects.run_creator_object import RunCreator
+from runner.run.processors.file_processor import FileProcessor
+from notifier.events import OperatorRequestEvent
+from notifier.models import JobGroup
+from notifier.tasks import send_notification
+
+LOGGER = logging.getLogger(__name__)
+
+ARGOS_NAME = "argos"
+ARGOS_VERSION = "1.7.0"
+ONCOKB_FG_SLUG = "oncokb-file-group"
+
+
+class ArgosReportOperator(Operator):
+    def get_jobs(self):
+        LOGGER.info("[%s] Running ArgosReportOperator", self.job_group_notifier_id)
+        self.annotations_path = (
+            "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/annotations/oncokb"
+        )
+
+        hf_run_id = self.run_ids[0]  # only one run in list
+        hf_run = Run.objects.get(id=hf_run_id)
+        project_prefix = hf_run.tags["project_prefix"]
+
+        input = self.gen_inputs(hf_run)
+        app = self.get_pipeline_id()
+        jobs = list()
+        pipeline = Pipeline.objects.get(id=app)
+        output_directory = self.gen_output_dir(pipeline, project_prefix)
+
+        name = "ARGOS Report: %s" % project_prefix
+        tags = {"name": name, "project_prefix": project_prefix, "app": app}
+        jobs.append(RunCreator(app=app, inputs=input, name=name, tags=tags, output_directory=output_directory))
+
+        run_date = datetime.now().strftime("%Y%m%d_%H:%M:%f")
+        beagle_version = __version__
+
+        self.send_message(
+            """
+            Writing HTML report files to {file_path}.
+
+            Run Date: {run_date}
+            Beagle Version: {beagle_version}
+            """.format(
+                file_path=output_directory, run_date=run_date, beagle_version=beagle_version
+            )
+        )
+
+        return jobs
+
+    def gen_output_dir(self, pipeline, project_prefix):
+        jg = JobGroup.objects.get(id=self.job_group_id)
+        jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+        output_directory = os.path.join(
+            pipeline.output_directory, ARGOS_NAME, project_prefix, ARGOS_VERSION, jg_created_date, "report"
+        )
+        return output_directory
+
+    def gen_inputs(self, hf_run):
+        request_id = hf_run.tags["project_prefix"]
+        ports = hf_run.port_set.all()
+        analysis_dir_path = dict()
+        portal_dir_path = dict()
+        for port in ports:
+            if port.name == "pairs":
+                normal_list, tumor_list = self.get_sample_ids(port.value)
+            if port.name == "analysis_dir":
+                analysis_dir_path = {
+                    "class": "Directory",
+                    "location": FileProcessor.parse_path_from_uri(port.value["location"]),
+                }
+            if port.name == "portal_dir":
+                portal_dir_path = {
+                    "class": "Directory",
+                    "location": FileProcessor.parse_path_from_uri(port.value["location"]),
+                }
+        input = dict()
+        input["request_id"] = request_id
+        input["tumor_ids"] = tumor_list
+        input["normal_ids"] = normal_list
+        input["portal_dir"] = portal_dir_path
+        input["analysis_dir"] = analysis_dir_path
+        input["oncokb_file"] = self.get_oncokb_file(self.annotations_path)
+        return input
+
+    def get_sample_ids(self, pair_dict):
+        normal_list = []
+        tumor_list = []
+        for single_pair in pair_dict:
+            normal_list.append(single_pair["normal_id"])
+            tumor_list.append(single_pair["tumor_id"])
+        return normal_list, tumor_list
+
+    def get_oncokb_file(self, annotations_path):
+        oncokb_dir = FileProcessor.parse_path_from_uri(annotations_path)
+        oncokb_files = os.listdir(oncokb_dir)
+        latest_file = sorted([f for f in oncokb_files if os.path.isfile(oncokb_dir + os.sep + f)])[-1]
+        oncokb_file_path = os.path.join(oncokb_dir, latest_file)
+        oncokb_file_registered = self._register_oncokb_file(oncokb_file_path)
+
+        oncokb_entry = {
+            "class": "File",
+            "location": "juno://" + oncokb_file_path,
+        }
+
+        return oncokb_entry
+
+    def _register_oncokb_file(self, oncokb_fp):
+        file_group = FileGroup.objects.get(slug=ONCOKB_FG_SLUG)
+        file_type = FileType.objects.filter(name="rds").first()
+        return self._create_file_obj(path=oncokb_fp, file_group=file_group, file_type=file_type)
+
+    def send_message(self, msg):
+        event = OperatorRequestEvent(self.job_group_notifier_id, msg)
+        e = event.to_dict()
+        send_notification.delay(e)
+
+    def _create_file_obj(self, path, file_group, file_type):
+        """
+        Tries to create a File from path provided
+        Returns True if file is created; False if file at path exists
+        """
+        file_exists = File.objects.filter(path=path).exists()
+        if not file_exists:
+            try:
+                f = File.objects.create(
+                    file_name=os.path.basename(path), path=path, file_group=file_group, file_type=file_type
+                )
+                f.save()
+                metadata = FileMetadata(file=f, metadata={})
+                metadata.save()
+                LOGGER.info("Adding OncoKB RDS file to database: %s" % path)
+                return True
+            except Exception as e:
+                LOGGER.error("Failed to create file %s. Error %s" % (path, str(e)))
+                raise Exception("Failed to create file %s. Error %s" % (path, str(e)))
+        else:
+            return False
diff --git a/runner/operator/chronos_outputs_operator/__init__.py b/runner/operator/chronos_outputs_operator/__init__.py
index f91546571..318cb84c1 100644
--- a/runner/operator/chronos_outputs_operator/__init__.py
+++ b/runner/operator/chronos_outputs_operator/__init__.py
@@ -1,5 +1,6 @@
 import io
 import os
+import shutil
 import logging
 import subprocess
 from django.conf import settings
@@ -34,6 +35,7 @@ def get_jobs(self):
                 raise Exception(f"Failed to copy bams for sample {ci_tag}")
             self.append_trace(run, destination_directory)
             self.append_bam_outputs(run, destination_directory)
+            self.clean_up_source_directory(run)
         return []
 
     def construct_metadata(self, run):
@@ -99,6 +101,10 @@ def append_trace(self, run, destination_directory):
     def append_bam_outputs(self, run, destination_directory):
         bam_outputs_path = os.path.join(run.output_directory, "pipeline_output.csv")
         bam_outputs_file_global = os.path.join(destination_directory, "pipeline_output.csv")
+        # Read result bam file
+        with open(bam_outputs_file_global, "r") as f:
+            results = f.readlines()
+            results = [line.rstrip() for line in results]
         with open(bam_outputs_path, "r") as f:
             # Read output bam file content without header
             trace_file_content = f.readlines()[1:]
@@ -111,6 +117,16 @@ def append_bam_outputs(self, run, destination_directory):
             file_name = elements[3].split("/")[-1]
             sample = elements[3].split("/")[-2]
             elements[3] = os.path.join(destination_directory, "bams", sample, file_name)
-            trace_file_result.append("\t".join(elements))
+            line = "\t".join(elements)
+            if line not in results:
+                trace_file_result.append(line)
+            else:
+                LOGGER.warning(f"{line} already in {bam_outputs_file_global}")
         with io.open(bam_outputs_file_global, "a") as f:
             f.writelines(trace_file_result)
+
+    def clean_up_source_directory(self, run):
+        try:
+            shutil.rmtree(run.output_directory)
+        except Exception as e:
+            logging.error(f"Failed to remove directory {run.output_directory}. {e}")
diff --git a/runner/operator/cmo_ch/v2_1_0/chip_var/input_template.json.jinja2 b/runner/operator/cmo_ch/v2_1_0/chip_var/input_template.json.jinja2
index ba5e4ebf7..cdbfa8b9d 100644
--- a/runner/operator/cmo_ch/v2_1_0/chip_var/input_template.json.jinja2
+++ b/runner/operator/cmo_ch/v2_1_0/chip_var/input_template.json.jinja2
@@ -53,15 +53,15 @@
   },
   "input_47kchpd_tsv_file": {
     "class": "File",
-	  "path": "juno:///work/cch/production/resources/ch_47k_impact/versions/v1/chpd47k_prevalence.tsv"
+	  "location": "juno:///work/cch/production/resources/ch_47k_impact/versions/v1/chpd47k_prevalence.tsv"
   },
   "input_panmeloid_tsv_file": {
     "class": "File",
-	  "path": "juno:///work/cch/production/resources/pan_myeloid/versions/v1/pan_myeloid_variant_count.tsv"
+	  "location": "juno:///work/cch/production/resources/pan_myeloid/versions/v1/pan_myeloid_variant_count.tsv"
   },
   "input_hotspot_tsv_file": {
     "class": "File",
-    "path": "juno:///work/cch/production/resources/ch_47k_impact/versions/v1/hotspots_47kchpd.tsv"
+    "location": "juno:///work/cch/production/resources/ch_47k_impact/versions/v1/hotspots_47kchpd.tsv"
   },
   "retain_info": "CNT,TUMOR_TYPE",
   "vardict_allele_frequency_threshold": 0,
diff --git a/runner/operator/cmo_ch/v2_1_0/nucleo/nucleo_operator.py b/runner/operator/cmo_ch/v2_1_0/nucleo/nucleo_operator.py
index f9356fd30..f86dd7665 100644
--- a/runner/operator/cmo_ch/v2_1_0/nucleo/nucleo_operator.py
+++ b/runner/operator/cmo_ch/v2_1_0/nucleo/nucleo_operator.py
@@ -6,6 +6,7 @@
 from collections import defaultdict
 
 from runner.operator.operator import Operator
+from runner.operator.helper import pair_samples
 from runner.run.objects.run_creator_object import RunCreator
 from file_system.repository.file_repository import FileRepository
 from django.conf import settings
@@ -42,10 +43,37 @@ def group_by_sample_id(samples):
     return sample_pairs
 
 
-def group_by_run(samples):
-    samples.sort(key=lambda s: s["path"].split("/")[-1])
-    fastqs = zip(samples[::2], samples[1::2])
-    return list(fastqs)
+def pair_samples(fastqs):
+    """
+    pair sample fastqs based on the delivery directory.
+
+    Parameters:
+        fastqs (list): A list of sample fastq files.
+
+    Returns:
+        list: A list of tuples containing paired fastqs for a sample
+    """
+    sample_pairs = []
+    expected_pair = set(["R1", "R2"])
+    # match R1 and R2 based on delivery directory
+    # sorting on file names is not enough as they are non-unique
+    for i, fastq in enumerate(fastqs):
+        dir = "/".join(fastq["path"].split("_R")[0:-1])
+        for compare in fastqs[i + 1 :]:
+            compare_dir = "/".join(compare["path"].split("_R")[0:-1])
+            if dir == compare_dir:
+                # check if R1 and R2 are present
+                r_check = set([fastq["metadata"]["R"], compare["metadata"]["R"]])
+                if r_check.issubset(expected_pair):
+                    # Keep ordering consistent
+                    if fastq["metadata"]["R"] == "R1":
+                        sample_pairs.append((fastq, compare))
+                    else:
+                        sample_pairs.append((compare, fastq))
+                else:
+                    sample_name = fastq["metadata"]["cmoSampleName"]
+                    raise Exception(f"Improper pairing for: {sample_name}")
+    return sample_pairs
 
 
 def calc_avg(sample_files, field):
@@ -81,7 +109,7 @@ def construct_sample_inputs(samples, request_id):
         sample_group = list(sample_group)
         sample_id = sample_group[0]["metadata"][settings.CMO_SAMPLE_NAME_METADATA_KEY]
 
-        fgbio_fastq_to_bam_input = group_by_run(sample_group)
+        fgbio_fastq_to_bam_input = pair_samples(sample_group)
         fgbio_fastq_to_bam_input = [
             [
                 {"class": "File", "location": "juno://" + s[0]["path"]},
diff --git a/runner/operator/copy_outputs_operator/v2_1_0/__init__.py b/runner/operator/copy_outputs_operator/v2_1_0/__init__.py
new file mode 100644
index 000000000..1961b941a
--- /dev/null
+++ b/runner/operator/copy_outputs_operator/v2_1_0/__init__.py
@@ -0,0 +1 @@
+from .copy_outputs_operator import CopyOutputsOperator
diff --git a/runner/operator/copy_outputs_operator/v2_1_0/construct_copy_outputs.py b/runner/operator/copy_outputs_operator/v2_1_0/construct_copy_outputs.py
new file mode 100644
index 000000000..65735f056
--- /dev/null
+++ b/runner/operator/copy_outputs_operator/v2_1_0/construct_copy_outputs.py
@@ -0,0 +1,186 @@
+import sys
+from pprint import pprint
+from runner.models import Port, Run
+from runner.run.processors.file_processor import FileProcessor
+from notifier.helper import generate_sample_data_content
+from django.conf import settings
+
+
+def get_argos_output_description():
+    output_description = {
+        "normal_bam": "bam",
+        "tumor_bam": "bam",
+        "clstats1": "qc",
+        "clstats2": "qc",
+        "md_metrics": "qc",
+        "as_metrics": "qc",
+        "hs_metrics": "qc",
+        "insert_metrics": "qc",
+        "insert_pdf": "qc",
+        "per_target_coverage": "qc",
+        "qual_metrics": "qc",
+        "qual_pdf": "qc",
+        "doc_basecounts": "qc",
+        "gcbias_pdf": "qc",
+        "gcbias_metrics": "qc",
+        "gcbias_summary": "qc",
+        "conpair_pileups": "qc",
+        "mutect_vcf": "vcf",
+        "mutect_callstats": "vcf",
+        "vardict_vcf": "vcf",
+        "combine_vcf": "vcf",
+        "annotate_vcf": "vcf",
+        "vardict_norm_vcf": "vcf",
+        "mutect_norm_vcf": "vcf",
+        "merged_file_unfiltered": "vcf",
+        "merged_file": "vcf",
+        "maf_file": "maf",
+        "portal_file": "maf",
+        "maf": "maf",
+        "snp_pileup": "pileup",
+        "disambiguate_summary": "disambiguate",
+    }
+    return output_description
+
+
+def create_cwl_file_obj(file_path):
+    cwl_file_obj = {"class": "File", "location": "juno://%s" % file_path}
+    return cwl_file_obj
+
+
+def get_file_obj(file_obj):
+    file_list = []
+    secondary_file_list = []
+    file_location = file_obj["location"].replace("file://", "")
+    file_cwl_obj = create_cwl_file_obj(file_location)
+    file_list.append(file_cwl_obj)
+    if "secondaryFiles" in file_obj:
+        for single_secondary_file in file_obj["secondaryFiles"]:
+            secondary_file_location = single_secondary_file["location"].replace("file://", "")
+            secondary_file_cwl_obj = create_cwl_file_obj(secondary_file_location)
+            secondary_file_list.append(secondary_file_cwl_obj)
+    return {"files": file_list, "secondary_files": secondary_file_list}
+
+
+def get_files_from_port(port_obj):
+    file_list = []
+    if isinstance(port_obj, list):
+        for single_port in port_obj:
+            if isinstance(single_port, list):
+                for single_file in single_port:
+                    file_list.append(get_file_obj(single_file))
+            else:
+                file_list.append(get_file_obj(single_port))
+    elif isinstance(port_obj, dict):
+        file_list.append(get_file_obj(port_obj))
+    return file_list
+
+
+def list_file_paths(file_obj_list):
+    list_of_files = []
+    for single_file_obj in file_obj_list:
+        list_of_files = list_of_files + single_file_obj["files"]
+        list_of_files = list_of_files + single_file_obj["secondary_files"]
+    return list_of_files
+
+
+def construct_copy_outputs_input(run_id_list):
+    input_json = {}
+    output_description = get_argos_output_description()
+    project_prefix = set()
+
+    for single_run_id in run_id_list:
+        port_list = Port.objects.filter(run=single_run_id)
+        for single_port in port_list:
+            if single_port.name == "project_prefix":
+                project_prefix.add(single_port.value)
+        for single_port in port_list:
+            port_name = single_port.name
+            if port_name in output_description:
+                port_type = output_description[port_name]
+                if port_type != "qc":
+                    port_files = get_files_from_port(single_port.value)
+                    if port_type not in input_json:
+                        input_json[port_type] = list_file_paths(port_files)
+                    else:
+                        input_json[port_type] = input_json[port_type] + list_file_paths(port_files)
+    input_json["project_prefix"] = "_".join(sorted(project_prefix))
+    return input_json
+
+
+def generate_sample_pairing_and_mapping_files(run_ids):
+    sample_pairing = ""
+    sample_mapping = ""
+
+    runs = Run.objects.filter(id__in=run_ids)
+
+    request_id_set = set()
+
+    files = list()
+
+    if runs:
+        pipeline = runs[0].app
+
+    for r in runs:
+        request_id_set.add(r.tags[settings.REQUEST_ID_METADATA_KEY])
+        tumor_sample = Port.objects.filter(run_id=r.id, name="tumor").first()
+        normal_sample = Port.objects.filter(run_id=r.id, name="normal").first()
+        tumor_sample_name = tumor_sample.db_value["ID"]
+        normal_sample_name = normal_sample.db_value["ID"]
+        for p in tumor_sample.db_value["R1"]:
+            sample_mapping += "\t".join([tumor_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in tumor_sample.db_value["R2"]:
+            sample_mapping += "\t".join([tumor_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in tumor_sample.db_value["zR1"]:
+            sample_mapping += "\t".join([tumor_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in tumor_sample.db_value["zR2"]:
+            sample_mapping += "\t".join([tumor_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in normal_sample.db_value["R1"]:
+            sample_mapping += "\t".join([normal_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in normal_sample.db_value["R2"]:
+            sample_mapping += "\t".join([normal_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in normal_sample.db_value["zR1"]:
+            sample_mapping += "\t".join([normal_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in normal_sample.db_value["zR2"]:
+            sample_mapping += "\t".join([normal_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+        for p in normal_sample.db_value["bam"]:
+            sample_mapping += "\t".join([normal_sample_name, FileProcessor.get_file_path(p["location"])]) + "\n"
+            files.append(FileProcessor.get_file_path(p["location"]))
+
+        sample_pairing += "\t".join([normal_sample_name, tumor_sample_name]) + "\n"
+
+    if runs:
+        data_clinical = generate_sample_data_content(
+            files, pipeline_name=pipeline.name, pipeline_github=pipeline.github, pipeline_version=pipeline.version
+        )
+
+    return sample_mapping, sample_pairing, data_clinical
+
+
+def get_output_directory_prefix(run_id_list):
+    run = Run.objects.get(id=run_id_list[0])
+    return run.tags.get("output_directory_prefix", None)
+
+
+def get_project_prefix(run_id_list):
+    project_prefix = set()
+    for single_run_id in run_id_list:
+        project_prefix_port = Port.objects.filter(run=single_run_id, name="project_prefix").first()
+        project_prefix.add(project_prefix_port.value)
+    return "_".join(sorted(project_prefix))
+
+
+if __name__ == "__main__":
+    run_id_list = []
+    for single_arg in sys.argv[1:]:
+        run_id_list.append(single_arg)
+    input_json = construct_copy_outputs_input(run_id_list)
+    pprint(input_json)
diff --git a/runner/operator/copy_outputs_operator/v2_1_0/copy_outputs_operator.py b/runner/operator/copy_outputs_operator/v2_1_0/copy_outputs_operator.py
new file mode 100644
index 000000000..173c99583
--- /dev/null
+++ b/runner/operator/copy_outputs_operator/v2_1_0/copy_outputs_operator.py
@@ -0,0 +1,143 @@
+"""
+CopyOutputsOperator
+
+Constructs input JSON for the copy outputs pipeline and then
+submits them as runs
+"""
+
+import os
+import uuid
+from pathlib import Path
+from django.conf import settings
+from notifier.models import JobGroup
+from file_system.models import File, FileGroup
+from file_system.serializers import CreateFileSerializer
+from runner.models import Pipeline, Run
+from runner.operator.operator import Operator
+from runner.run.objects.run_creator_object import RunCreator
+from .construct_copy_outputs import (
+    construct_copy_outputs_input,
+    generate_sample_pairing_and_mapping_files,
+    get_output_directory_prefix,
+    get_project_prefix,
+)
+
+
+class CopyOutputsOperator(Operator):
+    """
+    Constructs input JSON for the argos QC pipeline and then
+    submits them as runs
+    """
+
+    ARGOS_NAME = "argos"
+    ARGOS_VERSION = "1.7.0"
+
+    def get_jobs(self):
+        """
+        From self, retrieve relevant run IDs, build the input JSON for
+        the pipeline, and then submit them as jobs through the
+        RunCreator
+        """
+        run_ids = self.run_ids
+        input_json = construct_copy_outputs_input(run_ids)
+
+        mapping_file_content, pairing_file_content, data_clinical_content = generate_sample_pairing_and_mapping_files(
+            run_ids
+        )
+
+        number_of_runs = len(run_ids)
+        name = "ARGOS COPY OUTPUTS %s runs [%s,..] " % (number_of_runs, run_ids[0])
+
+        app = self.get_pipeline_id()
+        pipeline = Pipeline.objects.get(id=app)
+        project_prefix = input_json["project_prefix"]
+        output_directory_prefix = get_output_directory_prefix(self.run_ids)
+
+        mapping_file = self.write_to_file(
+            "sample_mapping.txt", mapping_file_content, project_prefix, pipeline.name, pipeline.version
+        )
+        pairing_file = self.write_to_file(
+            "sample_pairing.txt", pairing_file_content, project_prefix, pipeline.name, pipeline.version
+        )
+        data_clinical_file = self.write_to_file(
+            "sample_data_clinical.txt", data_clinical_content, project_prefix, pipeline.name, pipeline.version
+        )
+
+        input_json["meta"] = [mapping_file, pairing_file, data_clinical_file]
+
+        tags = {"run_ids": run_ids}
+
+        copy_outputs_job_data = {"app": app, "inputs": input_json, "name": name, "tags": tags}
+
+        """
+        If project_prefix and job_group_id, write output to a directory
+        that uses both
+
+        Also use argos version number for output instead of pipeline version
+        that's listed in Beagle
+        """
+
+        argos_run = Run.objects.get(id=run_ids[0])
+        argos_pipeline = argos_run.app
+
+        output_directory = None
+        if project_prefix:
+            tags["project_prefix"] = project_prefix
+            if self.job_group_id:
+                output_prefix = output_directory_prefix if output_directory_prefix else project_prefix
+                jg = JobGroup.objects.get(id=self.job_group_id)
+                jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+                output_directory = os.path.join(
+                    pipeline.output_directory, "argos", output_prefix, argos_pipeline.version, jg_created_date
+                )
+            copy_outputs_job_data["output_directory"] = output_directory
+        copy_outputs_job = [RunCreator(**copy_outputs_job_data)]
+        return copy_outputs_job
+
+    def write_to_file(self, fname, s, project_prefix, pipeline_name, pipeline_version):
+        """
+        Writes file to temporary location, then registers it to the temp file group
+        """
+        tmpdir = os.path.join(settings.BEAGLE_SHARED_TMPDIR, str(uuid.uuid4()))
+        Path(tmpdir).mkdir(parents=True, exist_ok=True)
+        output = os.path.join(tmpdir, fname)
+        with open(output, "w+") as fh:
+            fh.write(s)
+        os.chmod(output, 0o777)
+        self.register_tmp_file(output, project_prefix, pipeline_name, pipeline_version)
+        return {"class": "File", "location": "juno://" + output}
+
+    def register_tmp_file(self, path, project_prefix, pipeline_name, pipeline_version):
+        fname = os.path.basename(path)
+        temp_file_group_pk = FileGroup.objects.get(slug="temp").pk
+        file_type = "txt"
+        metadata = {"projectId": project_prefix, "pipelineName": pipeline_name, "pipelineVersion": pipeline_version}
+        data = {"path": path, "file_type": file_type, "metadata": metadata, "file_group": temp_file_group_pk}
+        try:
+            File.objects.get(path=path)
+        except:
+            print("Registering temp file %s" % path)
+            serializer = CreateFileSerializer(data=data)
+            if serializer.is_valid():
+                file = serializer.save()
+            else:
+                raise Exception("Failed to create temp file %s. Error %s" % (path, str(serializer.errors)))
+
+    def get_log_directory(self):
+        jg = JobGroup.objects.get(id=self.job_group_id)
+        jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+        app = self.get_pipeline_id()
+        pipeline = Pipeline.objects.get(id=app)
+        output_directory_prefix = get_project_prefix(self.run_ids)
+        output_directory = os.path.join(
+            pipeline.output_directory,
+            self.ARGOS_NAME,
+            output_directory_prefix,
+            self.ARGOS_VERSION,
+            jg_created_date,
+            "json",
+            pipeline.name,
+            pipeline.version,
+            "%s",
+        )
+        return output_directory
diff --git a/runner/operator/helix_filters/v23_1_0/__init__.py b/runner/operator/helix_filters/v23_1_0/__init__.py
new file mode 100644
index 000000000..931afa414
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/__init__.py
@@ -0,0 +1 @@
+from .helix_filters_operator import HelixFiltersOperator
diff --git a/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/OncotreeDataHandler.py b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/OncotreeDataHandler.py
new file mode 100644
index 000000000..961aeb172
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/OncotreeDataHandler.py
@@ -0,0 +1,92 @@
+import json
+import os
+import unicodedata
+import requests
+
+
+class OncotreeDataHandler:
+    def __init__(self):
+        self.oncotree = self.fetch_oncotree_data()
+
+    def fetch_oncotree_data(self):
+        oncotree_dir = os.path.dirname(__file__)
+        oncotree_json = os.path.join(oncotree_dir, "data/oncotree.json")
+        try:
+            request = requests.get("http://oncotree.mskcc.org/api/tumorTypes").json()
+        except:
+            request = "error"
+        if "error" in request:  # load from file if error
+            request = json.load(open(oncotree_json, "r"))
+        converted_data = self.convert_data_key(request)
+        return converted_data
+
+    def convert_data_key(self, data):
+        d = dict()
+        for node in data:
+            name = node["name"]
+            level = node["level"]
+            parent = node["parent"]
+            code = node["code"]
+            data_node = OncotreeNode(node)
+            d[code] = data_node
+        return d
+
+    def find_by_code(self, code):
+        code_upper = code.upper()
+        try:
+            return self.oncotree[code_upper]
+        except KeyError:
+            print("Code does not exist; defaulting to TISSUE")
+            return self.oncotree["TISSUE"]
+
+    def get_parent_list_by_code(self, code, result):
+        node = self.find_by_code(code)
+        parent = node.parent
+        result.append(node)
+        if parent is not None:
+            return self.get_parent_list_by_code(parent, result)
+        else:
+            return result
+
+    def find_shared_nodes_by_code_list(self, code_list):
+        list_of_sets = list()
+        for code in code_list:
+            node_data = set(self.get_parent_list_by_code(code, []))
+            list_of_sets.append(node_data)
+        ancestral_nodes = set.intersection(*list_of_sets)
+        return ancestral_nodes
+
+    def get_highest_level_shared_node(self, shared_nodes):
+        max_level = -1
+        node_to_return = None
+        for node in shared_nodes:
+            level = node.level
+            if node.level > max_level:
+                max_level = node.level
+                node_to_return = node
+        return node_to_return
+
+
+class OncotreeNode:
+    def __init__(self, data):
+        self.code = data["code"]
+        self.name = data["name"]
+        self.level = data["level"]
+        self.parent = data["parent"]
+
+    def __key(self):
+        return (self.code, self.name, self.level, self.parent)
+
+    def __eq(x, y):
+        return x.__key() == y.__key()
+
+    def __hash__(self):
+        return hash(self.__key())
+
+    def __repr__(self):
+        return "OncotreeNode(CODE: %s, NAME: %s, LEVEL: %i, PARENT: %s)" % (
+            self.code,
+            self.name,
+            self.level,
+            self.parent,
+        )
diff --git a/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/README.md b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/README.md
new file mode 100644
index 000000000..ed9bd8524
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/README.md
@@ -0,0 +1,2 @@
+# oncotree_data_handler
+Can search through a list of oncocodes and return the most common ancestor
diff --git a/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/__init__.py b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/data/oncotree.json b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/data/oncotree.json
new file mode 100644
index 000000000..7df60efb6
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/bin/oncotree_data_handler/data/oncotree.json
@@ -0,0 +1 @@
+[{"code": "MMB", "color": "Gray", "name": "Medullomyoblastoma", "mainType": "Embryonal Tumor", "externalReferences": {"UMLS": ["C0205833"], "NCI": ["C3706"]}, "tissue": "CNS/Brain", "children": {}, "parent": "EMBT", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "GCB", "color": "LimeGreen", "name": "Germinal Center B-Cell Type", "mainType": "Mature B-Cell Neoplasms", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "DLBCLNOS", "history": [], "level": 6, "revocations": [], "precursors": []}, {"code": "SBLU", "color": "LimeGreen", "name": "Splenic B-Cell Lymphoma/Leukemia, Unclassifiable", "mainType": "Mature B-Cell Neoplasms", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "MBN", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "OHNCA", "color": "DarkRed", "name": "Head and Neck Carcinoma, Other", "mainType": "Head and Neck Cancer", "externalReferences": {}, "tissue": "Head and Neck", "children": {}, "parent": "HEAD_NECK", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "PAOS", "color": "White", "name": "Parosteal Osteosarcoma", "mainType": "Bone Cancer", "externalReferences": {"UMLS": ["C0206642"], "NCI": ["C8969"]}, "tissue": "Bone", "children": {}, "parent": "OS", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "TMDS", "color": "LightSalmon", "name": "Therapy-Related Myelodysplastic Syndrome", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "TMN", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "ARMS", "color": "LightYellow", "name": "Alveolar Rhabdomyosarcoma", "mainType": "Soft Tissue Sarcoma", "externalReferences": {"UMLS": ["C0206655"], "NCI": ["C3749"]}, "tissue": "Soft Tissue", "children": {}, "parent": "RMS", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "SCST", "color": "LightBlue", "name": "Sex Cord Stromal Tumor", "mainType": "Sex Cord Stromal Tumor", "externalReferences": {"UMLS": ["C0600113"], "NCI": ["C4862"]}, "tissue": "Ovary/Fallopian Tube", "children": {}, "parent": "OVARY", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "ITLPDGI", "color": "LimeGreen", "name": "Indolent T-Cell Lymphoproliferative Disorder of the GI Tract", "mainType": "Mature T and NK Neoplasms", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "MTNN", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "MBC", "color": "HotPink", "name": "Metaplastic Breast Cancer", "mainType": "Breast Cancer", "externalReferences": {"UMLS": ["C1334708"], "NCI": ["C5164"]}, "tissue": "Breast", "children": {}, "parent": "BREAST", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "AWDNET", "color": "SaddleBrown", "name": "Well-Differentiated Neuroendocrine Tumor of the Appendix", "mainType": "Gastrointestinal Neuroendocrine Tumor", "externalReferences": {"UMLS": ["C3272767"], "NCI": ["C96422"]}, "tissue": "Bowel", "children": {}, "parent": "GINET", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "AMLCBFBMYH11", "color": "LightSalmon", "name": "AML with inv(16)(p13.1q22) or t(16;16)(p13.1;q22);CBFB-MYH11", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AMLRGA", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "ROCY", "color": "Orange", "name": "Renal Oncocytoma", "mainType": "Renal Cell Carcinoma", "externalReferences": {"UMLS": ["C0346255"], "NCI": ["C4526"]}, "tissue": "Kidney", "children": {}, "parent": "NCCRCC", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "LAM", "color": "Gainsboro", "name": "Pulmonary Lymphangiomyomatosis", "mainType": "Non-Small Cell Lung Cancer", "externalReferences": {"UMLS": ["C0349649"], "NCI": ["C38153"]}, "tissue": "Lung", "children": {}, "parent": "LUNG", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "SBL", "color": "DarkRed", "name": "Sialoblastoma", "mainType": "Sialoblastoma", "externalReferences": {}, "tissue": "Head and Neck", "children": {}, "parent": "HEAD_NECK", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "AWM", "color": "LightSalmon", "name": "AML without Maturation", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AMLNOS", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "AMLMRC", "color": "LightSalmon", "name": "AML with Myelodysplasia-Related Changes", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AML", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "CABC", "color": "Teal", "name": "Cervical Adenoid Basal Carcinoma", "mainType": "Cervical Cancer", "externalReferences": {"UMLS": ["C1516403"], "NCI": ["C40213"]}, "tissue": "Cervix", "children": {}, "parent": "CERVIX", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "PCGP", "color": "Gray", "name": "Craniopharyngioma, Papillary Type", "mainType": "Sellar Tumor", "externalReferences": {}, "tissue": "CNS/Brain", "children": {}, "parent": "SELT", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "SCCO", "color": "LightBlue", "name": "Small Cell Carcinoma of the Ovary", "mainType": "Ovarian Cancer", "externalReferences": {"UMLS": ["C2212006"], "NCI": ["C27390"]}, "tissue": "Ovary/Fallopian Tube", "children": {}, "parent": "OVT", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "LGT", "color": "Green", "name": "Lacrimal Gland Tumor", "mainType": "Lacrimal Gland Tumor", "externalReferences": {}, "tissue": "Eye", "children": {}, "parent": "EYE", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "GBM", "color": "Gray", "name": "Glioblastoma Multiforme", "mainType": "Glioma", "externalReferences": {}, "tissue": "CNS/Brain", "children": {}, "parent": "GB", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "BL", "color": "LimeGreen", "name": "Burkitt Lymphoma", "mainType": "Mature B-Cell Neoplasms", "externalReferences": {"UMLS": ["C0006413"], "NCI": ["C2912"]}, "tissue": "Lymphoid", "children": {}, "parent": "MBN", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "MFS", "color": "LightYellow", "name": "Myxofibrosarcoma", "mainType": "Soft Tissue Sarcoma", "externalReferences": {"UMLS": ["C0334454"], "NCI": ["C6496"]}, "tissue": "Soft Tissue", "children": {}, "parent": "SOFT_TISSUE", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "AN", "color": "Black", "name": "Atypical Nevus", "mainType": "Skin Cancer, Non-Melanoma", "externalReferences": {}, "tissue": "Skin", "children": {}, "parent": "SKIN", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "SELT", "color": "Gray", "name": "Sellar Tumor", "mainType": "Sellar Tumor", "externalReferences": {"UMLS": ["C0748616"], "NCI": ["C4944"]}, "tissue": "CNS/Brain", "children": {}, "parent": "BRAIN", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "AMOL", "color": "LightSalmon", "name": "Acute Monoblastic/Monocytic Leukemia", "mainType": "Leukemia", "externalReferences": {"UMLS": ["C0023465"], "NCI": ["C4861"]}, "tissue": "Myeloid", "children": {}, "parent": "AMLNOS", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "PERL", "color": "LightSalmon", "name": "Pure Erythroid Leukemia", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AMLNOS", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "AMLCEBPA", "color": "LightSalmon", "name": "AML with Biallelic Mutations of CEBPA", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AMLRGA", "history": [], "level": 5, "revocations": [], "precursors": []}, {"code": "UASC", "color": "PeachPuff", "name": "Uterine Adenosquamous Carcinoma", "mainType": "Endometrial Cancer", "externalReferences": {"UMLS": ["C0346202"], "NCI": ["C4519"]}, "tissue": "Uterus", "children": {}, "parent": "UCEC", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "LNM", "color": "LimeGreen", "name": "Lymphoid Neoplasm", "mainType": "Lymphatic Cancer, NOS", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "LYMPH", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "PXA", "color": "Gray", "name": "Pleomorphic Xanthoastrocytoma", "mainType": "Glioma", "externalReferences": {"UMLS": ["C0334586"], "NCI": ["C4323"]}, "tissue": "CNS/Brain", "children": {}, "parent": "ENCG", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "PMBL", "color": "LimeGreen", "name": "Primary Mediastinal (Thymic) Large B-Cell Lymphoma", "mainType": "Mature B-Cell Neoplasms", "externalReferences": {"UMLS": ["C1292754"], "NCI": ["C9280"]}, "tissue": "Lymphoid", "children": {}, "parent": "MBN", "history": ["MBCL"], "level": 5, "revocations": [], "precursors": []}, {"code": "BIALCL", "color": "LimeGreen", "name": "Breast Implant-Associated Anaplastic Large-Cell Lymphoma", "mainType": "Mature T and NK Neoplasms", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "ALCL", "history": [], "level": 6, "revocations": [], "precursors": []}, {"code": "SRAP", "color": "SaddleBrown", "name": "Signet Ring Cell Type of the Appendix", "mainType": "Appendiceal Cancer", "externalReferences": {"UMLS": ["C1711320"], "NCI": ["C43554"]}, "tissue": "Bowel", "children": {}, "parent": "APAD", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "LXSC", "color": "DarkRed", "name": "Larynx Squamous Cell Carcinoma", "mainType": "Head and Neck Cancer", "externalReferences": {"UMLS": ["C0280324"], "NCI": ["C4044"]}, "tissue": "Head and Neck", "children": {}, "parent": "HNSC", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "ADRENAL_GLAND", "color": "Purple", "name": "Adrenal Gland", "mainType": "Adrenal Gland Cancer, NOS", "externalReferences": {"UMLS": ["C0001625"], "NCI": ["C12666"]}, "tissue": "Adrenal Gland", "children": {}, "parent": "TISSUE", "history": [], "level": 1, "revocations": [], "precursors": []}, {"code": "WM", "color": "LimeGreen", "name": "Waldenstrom Macroglobulinemia", "mainType": "Mature B-Cell Neoplasms", "externalReferences": {"UMLS": ["C0024419"], "NCI": ["C80307"]}, "tissue": "Lymphoid", "children": {}, "parent": "LPL", "history": [], "level": 6, "revocations": [], "precursors": []}, {"code": "RHM", "color": "Gray", "name": "Rhabdoid Meningioma", "mainType": "CNS Cancer", "externalReferences": {"UMLS": ["C0259786"], "NCI": ["C6909"]}, "tissue": "CNS/Brain", "children": {}, 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[], "precursors": []}, {"code": "UPECOMA", "color": "PeachPuff", "name": "Uterine Perivascular Epithelioid Cell Tumor", "mainType": "Uterine Sarcoma", "externalReferences": {"UMLS": ["C1519862"], "NCI": ["C40180"]}, "tissue": "Uterus", "children": {}, "parent": "USARC", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "MPRDS", "color": "LightSalmon", "name": "Myeloid Proliferations Related to Down Syndrome", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AML", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "SCRMS", "color": "LightYellow", "name": "Spindle Cell Rhabdomyosarcoma", "mainType": "Soft Tissue Sarcoma", "externalReferences": {"UMLS": ["C1266134"], "NCI": ["C6519"]}, "tissue": "Soft Tissue", "children": {}, "parent": "RMS", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "MCN", "color": "Purple", "name": "Mucinous Cystic Neoplasm", "mainType": "Pancreatic Cancer", "externalReferences": {"UMLS": ["C1518872"], "NCI": ["C41247"]}, "tissue": "Pancreas", "children": {}, "parent": "PACT", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "GCCAP", "color": "SaddleBrown", "name": "Goblet Cell Carcinoid of the Appendix", "mainType": "Appendiceal Cancer", "externalReferences": {"UMLS": ["C0205695"]}, "tissue": "Bowel", "children": {}, "parent": "APAD", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "AMBL", "color": "Gray", "name": "Large Cell/Anaplastic Medulloblastoma", "mainType": "Embryonal Tumor", "externalReferences": {"UMLS": ["C1266180"], "NCI": ["C6904"]}, "tissue": "CNS/Brain", "children": {}, "parent": "EMBT", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "CHGL", "color": "Gray", "name": "Chordoid Glioma of the Third Ventricle", "mainType": "Miscellaneous Neuroepithelial Tumor", "externalReferences": {"UMLS": ["C1322252"], "NCI": ["C5592"]}, "tissue": "CNS/Brain", "children": {}, "parent": "MNET", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "RCC", "color": "Orange", "name": "Renal Cell Carcinoma", "mainType": "Renal Cell Carcinoma", "externalReferences": {"UMLS": ["C0007134"], "NCI": ["C9385"]}, "tissue": "Kidney", "children": {}, "parent": "KIDNEY", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "MGUSIGM", "color": "LimeGreen", "name": "IgM", "mainType": "Mature B-Cell Neoplasms", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "MGUS", "history": [], "level": 6, "revocations": [], "precursors": []}, {"code": "BRCANOS", "color": "HotPink", "name": "Breast Invasive Cancer, NOS", "mainType": "Breast Cancer", "externalReferences": {}, "tissue": "Breast", "children": {}, "parent": "BRCA", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "VULVA", "color": "Purple", "name": "Vulva/Vagina", "mainType": "Vulvar/Vaginal Cancer, NOS", "externalReferences": {"UMLS": ["C0042993"], "NCI": ["C12408"]}, "tissue": "Vulva/Vagina", "children": {}, "parent": "TISSUE", "history": [], "level": 1, "revocations": [], "precursors": []}, {"code": "SSRCC", "color": "LightSkyBlue", "name": "Signet Ring Cell Carcinoma of the Stomach", "mainType": "Esophagogastric Cancer", "externalReferences": {"UMLS": ["C1335965"], "NCI": ["C5250"]}, "tissue": "Esophagus/Stomach", "children": {}, "parent": "USTAD", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "DASTR", "color": "Gray", "name": "Diffuse Astrocytoma", "mainType": "Glioma", "externalReferences": {"UMLS": ["C0280785"], "NCI": ["C7173"]}, "tissue": "CNS/Brain", "children": {}, "parent": "DIFG", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "MSTAD", "color": "LightSkyBlue", "name": "Mucinous Stomach Adenocarcinoma", "mainType": "Esophagogastric Cancer", "externalReferences": {"UMLS": ["C1334809"], "NCI": ["C5248"]}, "tissue": "Esophagus/Stomach", "children": {}, "parent": "STAD", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "ETPLL", "color": "LimeGreen", "name": "Early T-Cell Precursor Lymphoblastic Leukemia", "mainType": "T-Lymphoblastic Leukemia/Lymphoma", "externalReferences": {}, "tissue": "Lymphoid", "children": {}, "parent": "TLL", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "PTES", "color": "LightYellow", "name": "Proximal-Type Epithelioid Sarcoma", "mainType": "Soft Tissue Sarcoma", "externalReferences": {"UMLS": ["C1335563"], "NCI": ["C27472"]}, "tissue": "Soft Tissue", "children": {}, "parent": "EPIS", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "PPM", "color": "Gray", "name": "Papillary Meningioma", "mainType": "CNS Cancer", "externalReferences": {"UMLS": ["C3163622"], "NCI": ["C3904"]}, "tissue": "CNS/Brain", "children": {}, "parent": "MNGT", "history": [], "level": 3, "revocations": [], "precursors": []}, {"code": "VSC", "color": "Purple", "name": "Squamous Cell Carcinoma of the Vulva/Vagina", "mainType": "Vaginal Cancer", "externalReferences": {"UMLS": ["C0238518"], "NCI": ["C7736"]}, "tissue": "Vulva/Vagina", "children": {}, "parent": "VULVA", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "OOVC", "color": "LightBlue", "name": "Ovarian Cancer, Other", "mainType": "Ovarian Cancer", "externalReferences": {}, "tissue": "Ovary/Fallopian Tube", "children": {}, "parent": "OVARY", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "LCH", "color": "LightSalmon", "name": "Langerhans Cell Histiocytosis", "mainType": "Histiocytosis", "externalReferences": {"UMLS": ["C0019621"], "NCI": ["C3107"]}, "tissue": "Myeloid", "children": {}, "parent": "HDCN", "history": [], "level": 4, "revocations": [], "precursors": []}, {"code": "PSEC", "color": "Green", "name": "Peritoneal Serous Carcinoma", "mainType": "Peritoneal Cancer, NOS", "externalReferences": {}, "tissue": "Peritoneum", "children": {}, "parent": "PERITONEUM", "history": [], "level": 2, "revocations": [], "precursors": []}, {"code": "AM", "color": "LightSalmon", "name": "AML with Maturation", "mainType": "Leukemia", "externalReferences": {}, "tissue": "Myeloid", "children": {}, "parent": "AMLNOS", "history": [], "level": 5, "revocations": [], "precursors": []}]
\ No newline at end of file
diff --git a/runner/operator/helix_filters/v23_1_0/construct_helix_filters_input.py b/runner/operator/helix_filters/v23_1_0/construct_helix_filters_input.py
new file mode 100644
index 000000000..85d06b7f4
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/construct_helix_filters_input.py
@@ -0,0 +1,383 @@
+"""
+Main module that builds the JSON that needs to be submitted
+to the pipeline executor
+"""
+import logging
+import os
+import sys
+import json
+from runner.models import Port, Run
+from runner.run.processors.file_processor import FileProcessor
+from file_system.repository.file_repository import FileRepository
+from notifier.helper import generate_sample_data_content
+from .bin.oncotree_data_handler.OncotreeDataHandler import OncotreeDataHandler
+from django.db.models import Q
+from django.conf import settings
+
+WORKDIR = os.path.dirname(os.path.abspath(__file__))
+LOGGER = logging.getLogger(__name__)
+
+
+def load_references():
+    """
+    Loads reference data from the resources JSON
+    """
+    data = json.load(open(os.path.join(WORKDIR, "reference_jsons/helix_filters_resources.json"), "rb"))
+    return data
+
+
+def get_baits_and_targets(assay, helix_filters_resources):
+    """
+    From value in assay, retrieve target files from helix_filters_resources
+    """
+    targets = helix_filters_resources["targets"]
+
+    target_assay = assay
+
+    if assay.find("HemePACT_v4") > -1:
+        target_assay = "HemePACT_v4_BAITS"
+
+    if assay.find("IMPACT505") > -1:
+        target_assay = "IMPACT505_b37"
+    if assay.find("IMPACT410") > -1:
+        target_assay = "IMPACT410_b37"
+    if assay.find("IMPACT468") > -1:
+        target_assay = "IMPACT468_b37"
+    if assay.find("IMPACT341") > -1:
+        target_assay = "IMPACT341_b37"
+    if assay.find("IDT_Exome_v1_FP") > -1:
+        target_assay = "IDT_Exome_v1_FP_b37"
+    if assay.find("IMPACT468+08390") > -1:
+        target_assay = "IMPACT468_08390"
+    if assay.find("IMPACT468+Poirier_RB1_intron_V2") > -1:
+        target_assay = "IMPACT468_08050"
+
+    if target_assay in targets:
+        return {"class": "File", "location": str(targets[target_assay]["targets_list"])}
+    else:
+        error_msg = "ERROR: Targets for Assay not found in helix_filters_resources.json: %s" % assay
+        LOGGER.error(error_msg)
+
+
+def create_cwl_file_obj(file_path):
+    """
+    Given a filepath, return a dictionary with class File and JUNO-specific URI
+    """
+    cwl_file_obj = {"class": "File", "location": "juno://%s" % file_path}
+    return cwl_file_obj
+
+
+def get_file_obj(file_obj):
+    """
+    Given file_obj, construct a dictionary of class File, that file's
+    JUNO-specific URI file path, and a list of secondary files with
+    JUNO-specific URI file paths
+    """
+    secondary_file_list = []
+    file_location = file_obj["location"].replace("file://", "")
+    if "secondaryFiles" in file_obj:
+        for single_secondary_file in file_obj["secondaryFiles"]:
+            secondary_file_location = single_secondary_file["location"].replace("file://", "")
+            secondary_file_cwl_obj = create_cwl_file_obj(secondary_file_location)
+            secondary_file_list.append(secondary_file_cwl_obj)
+    file_cwl_obj = create_cwl_file_obj(file_location)
+    if secondary_file_list:
+        file_cwl_obj["secondaryFiles"] = secondary_file_list
+    return file_cwl_obj
+
+
+def get_files_from_port(port_obj):
+    file_list = []
+    if isinstance(port_obj, list):
+        for single_file in port_obj:
+            if isinstance(single_file, list):
+                file_list.append(get_files_from_port(single_file))
+            else:
+                file_list.append(get_file_obj(single_file))
+    elif isinstance(port_obj, dict):
+        file_list.append(get_file_obj(port_obj))
+    return file_list
+
+
+def list_keys_for_filters():
+    """
+    Returns a list of keys expected in the JSON to be submitted to the pipeline; these
+    keys will have a list of values in the JSON
+    """
+    keys = [
+        "mutation_maf_files",
+        "mutation_svs_maf_files",
+        "mutation_svs_txt_files",
+        "targets_list",
+        "tumor_bam_files",
+        "normal_bam_files",
+    ]
+    return set(keys)
+
+
+def single_keys_for_filters():
+    """
+    Returns a list of keys expected in the JSON to be submitted to the pipeline; these
+    keys will have a single of values in the JSON
+    """
+    keys = [
+        "assay",
+        "project_prefix",
+        "is_impact",
+        "analyst_file",
+        "portal_file",
+        "portal_CNA_file",
+        "analysis_gene_cna_file",
+    ]
+    return set(keys)
+
+
+def construct_helix_filters_input(argos_run_id_list):
+    """
+    Main function. From a list of run IDs, build a JSON that combines
+    the runs data into one JSON expected by the helix filters pipeline
+    """
+    input_json = {}
+    pairs = []
+    list_keys = list_keys_for_filters()
+    single_keys = single_keys_for_filters()
+
+    for key in list_keys:
+        input_json[key] = list()
+
+    for single_run_id in argos_run_id_list:
+        port_list = Port.objects.filter(run=single_run_id)
+        pair_info = {}
+        for single_port in port_list:
+            name = single_port.name
+            value = single_port.value
+            if name == "maf":
+                input_json["mutation_maf_files"].append(get_file_obj(value))
+                pair_info["pair_maf"] = get_file_obj(value)
+            if name == "snp_pileup":
+                pair_info["snp_pileup"] = get_file_obj(value)
+            if name == "normal":
+                normal_id = value["ID"]
+                pair_info["normal_id"] = normal_id
+            if name == "tumor":
+                tumor_id = value["ID"]
+                pair_info["tumor_id"] = tumor_id
+            if name == "maf_file":
+                input_json["mutation_svs_maf_files"].append(get_file_obj(value))
+            if name == "portal_file":
+                input_json["mutation_svs_txt_files"].append(get_file_obj(value))
+            if name == "project_prefix":
+                input_json[name] = single_port.value
+            if name == "assay":
+                if "impact" in single_port.value.lower():
+                    input_json["is_impact"] = True
+                else:
+                    input_json["is_impact"] = False
+                input_json["assay"] = single_port.value
+            if name == "tumor_bam":
+                input_json["tumor_bam_files"].append(get_file_obj(value))
+            if name == "normal_bam":
+                input_json["normal_bam_files"].append(get_file_obj(value))
+        pair_info["pair_id"] = "{}.{}".format(pair_info["tumor_id"], pair_info["normal_id"])
+        pairs.append(pair_info)
+
+    references = convert_references(input_json["assay"])
+    input_json.update(references)
+
+    # some default values
+    project_prefix = input_json["project_prefix"]
+    input_json["project_id"] = project_prefix
+    input_json["project_short_name"] = project_prefix
+    input_json["project_name"] = project_prefix
+    input_json["project_description"] = project_prefix
+    input_json["cancer_study_identifier"] = project_prefix
+
+    # Gotta retrieve extra info that's not in the run porto have to query the database
+    #
+    # Get oncotree codes from samples in project_prefix/request_id FileMetadata
+    # will need to change project_prefix to request_id values everywhere, but for now we assume
+    # there is only one project_prefix/request_id per helix_filters run
+    input_json["cancer_type"] = get_oncotree_codes(project_prefix)
+    input_json["argos_version_string"] = get_argos_pipeline_version(argos_run_id_list)
+    input_json["project_pi"] = get_project_pi(argos_run_id_list)
+    input_json["request_pi"] = get_request_pi(argos_run_id_list)
+
+    # facets input
+    input_json["pairs"] = pairs
+
+    # generate data_clinical file
+    input_json["data_clinical_file"] = create_data_clinical_file(argos_run_id_list)
+
+    # need this for aion
+    input_json["lab_head_email"] = get_lab_head_email(argos_run_id_list)
+
+    return input_json
+
+
+def create_data_clinical_file(run_id_list):
+    files = list()
+    pipeline_names = set()
+    pipeline_githubs = set()
+    pipeline_versions = set()
+    for run_id in run_id_list:
+        argos_run = Run.objects.get(id=run_id)
+        pipeline = argos_run.app
+        pipeline_names.add(pipeline.name)
+        pipeline_githubs.add(pipeline.github)
+        pipeline_versions.add(pipeline.version)
+        files = files + get_files_from_run(argos_run)
+    data_clinical_content = generate_sample_data_content(
+        files,
+        pipeline_name=",".join(pipeline_names),
+        pipeline_github=",".join(pipeline_githubs),
+        pipeline_version=",".join(pipeline_versions),
+    )
+    data_clinical_content = data_clinical_content.strip()
+    return {"class": "File", "basename": "sample_data_clinical.txt", "contents": data_clinical_content}
+
+
+def get_files_from_run(r):
+    files = list()
+    inp_port = Port.objects.filter(run_id=r.id, name="tumor").first()
+    for p in inp_port.db_value["R1"]:
+        files.append(FileProcessor.get_file_path(p["location"]))
+    for p in inp_port.db_value["R2"]:
+        files.append(FileProcessor.get_file_path(p["location"]))
+    for p in inp_port.db_value["zR1"]:
+        files.append(FileProcessor.get_file_path(p["location"]))
+    for p in inp_port.db_value["zR2"]:
+        files.append(FileProcessor.get_file_path(p["location"]))
+    for p in inp_port.db_value["bam"]:
+        files.append(FileProcessor.get_file_path(p["location"]))
+    return files
+
+
+def build_request_ids_query(data):
+    """
+    Build complex Q object run id query from given data
+
+    Only does OR queries, as seen in line
+
+       query |= item
+
+    """
+    data_query_set = [Q(("metadata__{}".format(settings.REQUEST_ID_METADATA_KEY), value)) for value in set(data)]
+    query = data_query_set.pop()
+    for item in data_query_set:
+        query |= item
+    return query
+
+
+def get_project_pi(run_id_list):
+    project_pis = set()
+    for run_id in run_id_list:
+        argos_run = Run.objects.get(id=run_id)
+        project_pi = format_msk_id(argos_run.tags["labHeadEmail"])
+        project_pis.add(project_pi)
+    return ",".join(list(sorted(project_pis)))
+
+
+def get_lab_head_email(run_id_list):
+    lab_head_emails = set()
+    for run_id in run_id_list:
+        argos_run = Run.objects.get(id=run_id)
+        lab_head_email = argos_run.tags["labHeadEmail"]
+        lab_head_emails.add(lab_head_email)
+    return ",".join(list(sorted(lab_head_emails)))
+
+
+def get_request_pi(run_id_list):
+    request_pis = set()
+    files = FileRepository.all()
+    all_request_ids = set()
+    # reducing number of queries
+    for run_id in run_id_list:
+        argos_run = Run.objects.get(id=run_id)
+        run_request_id = argos_run.tags[settings.REQUEST_ID_METADATA_KEY]
+        all_request_ids.add(run_request_id)
+    for request_id in all_request_ids:
+        investigator_emails = FileRepository.filter(
+            queryset=files, metadata={settings.REQUEST_ID_METADATA_KEY: request_id}
+        ).values_list("metadata__investigatorEmail", flat=True)
+        request_pis = request_pis.union(set(investigator_emails))
+    request_pis_final = list()
+    for request_pi in request_pis:
+        if request_pi:
+            request_pis_final.append(format_msk_id(request_pi))
+    return ",".join(request_pis_final)
+
+
+def get_argos_pipeline_version(run_id_list):
+    versions = set()
+    for run_id in run_id_list:
+        argos_run = Run.objects.get(id=run_id)
+        versions.add(argos_run.app.version)
+    return "_".join(list(sorted(versions)))
+
+
+def format_msk_id(email_address):
+    return email_address.split("@")[0]
+
+
+def convert_references(assay):
+    """
+    Return a dictionary of references based on "assay" for targets_list
+    """
+    helix_filters_resources = load_references()
+    references = dict()
+    targets_list = get_baits_and_targets(assay, helix_filters_resources)
+    references["assay_coverage"] = str(get_assay_coverage(assay, helix_filters_resources))
+    references["targets_list"] = targets_list
+    references["known_fusions_file"] = {"class": "File", "location": str(helix_filters_resources["known_fusions_file"])}
+    references["IMPACT_gene_list"] = {"class": "File", "location": str(helix_filters_resources["IMPACT_gene_list"])}
+    references["microsatellites_file"] = {
+        "class": "File",
+        "location": str(helix_filters_resources["microsatellites_file"]),
+    }
+    return references
+
+
+def get_assay_coverage(assay, helix_filters_resources):
+    assay_coverages_list = helix_filters_resources["assay_tmb_coverage_values"]
+    assay_coverage = 0
+    for key in assay_coverages_list.keys():
+        curr_assay = key.lower()
+        if curr_assay in assay.lower():
+            assay_coverage = assay_coverages_list[key]
+    return assay_coverage
+
+
+def get_oncotree_codes(request_id):
+    oncotree_dh = OncotreeDataHandler()
+    files = FileRepository.all()
+    oncotree_codes_tmp = set(
+        FileRepository.filter(queryset=files, metadata={settings.REQUEST_ID_METADATA_KEY: request_id}).values_list(
+            "metadata__{}".format(settings.ONCOTREE_METADATA_KEY), flat=True
+        )
+    )
+    oncotree_codes = list()
+    for val in oncotree_codes_tmp:
+        if val:
+            oncotree_codes.append(val)
+    if not oncotree_codes:  # hack; if there are no oncotree codes, just say it's mixed
+        return "mixed"
+    shared_nodes = oncotree_dh.find_shared_nodes_by_code_list(oncotree_codes)
+    common_anc = oncotree_dh.get_highest_level_shared_node(shared_nodes)
+    if common_anc.code.lower() == "tissue":
+        common_anc.code = "mixed"
+    return common_anc.code.lower()
+
+
+def get_project_prefix(run_id_list):
+    project_prefix = set()
+    for single_run_id in run_id_list:
+        project_prefix_port = Port.objects.filter(run=single_run_id, name="project_prefix").first()
+        project_prefix.add(project_prefix_port.value)
+    return "_".join(sorted(project_prefix))
+
+
+if __name__ == "__main__":
+    RUN_ID_LIST = []
+    for single_arg in sys.argv[1:]:
+        RUN_ID_LIST.append(single_arg)
+    INPUT_JSON = construct_helix_filters_input(RUN_ID_LIST)
diff --git a/runner/operator/helix_filters/v23_1_0/helix_filters_operator.py b/runner/operator/helix_filters/v23_1_0/helix_filters_operator.py
new file mode 100644
index 000000000..9fa6a0e0b
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/helix_filters_operator.py
@@ -0,0 +1,103 @@
+"""
+Helix Filters Operator
+
+Constructs input JSON for the Helix Filters pipeline and then
+submits them as runs
+"""
+import os
+import logging
+from notifier.models import JobGroup
+from runner.models import Pipeline, Run
+from runner.operator.operator import Operator
+from runner.run.objects.run_creator_object import RunCreator
+from .construct_helix_filters_input import construct_helix_filters_input, get_project_prefix
+
+LOGGER = logging.getLogger(__name__)
+
+
+class HelixFiltersOperator(Operator):
+    """
+    Constructs input JSON for the Helix Filters pipeline and then
+    submits them as runs
+    """
+
+    ARGOS_NAME = "argos"
+    ARGOS_VERSION = "1.7.0"
+
+    def get_jobs(self):
+        """
+        From self, retrieve relevant run IDs, build the input JSON for
+        the pipeline, and then submit them as jobs through the
+        RunCreator
+        """
+        argos_run_ids = self.run_ids
+        input_json = construct_helix_filters_input(argos_run_ids)
+        number_of_runs = len(argos_run_ids)
+        name = "HELIX FILTERS OUTPUTS %s runs [%s,..] " % (number_of_runs, argos_run_ids[0])
+
+        app = self.get_pipeline_id()
+        pipeline = Pipeline.objects.get(id=app)
+        pipeline_version = pipeline.version
+        project_prefix = input_json["project_prefix"]
+        lab_head_email = input_json["lab_head_email"]
+        input_json["helix_filter_version"] = pipeline_version
+        input_json = self.add_output_file_names(input_json, pipeline_version)
+        tags = {"project_prefix": project_prefix, "argos_run_ids": argos_run_ids, "labHeadEmail": lab_head_email}
+
+        helix_filters_outputs_job_data = {"app": app, "inputs": input_json, "name": name, "tags": tags}
+
+        """
+        If project_prefix and job_group_id, write output to a directory
+        that uses both
+
+        Going by argos pipeline version id, assuming all runs use the same argos version
+        """
+        argos_run = Run.objects.get(id=argos_run_ids[0])
+        argos_pipeline = argos_run.app
+
+        output_directory = None
+        if project_prefix:
+            tags["project_prefix"] = project_prefix
+            if self.job_group_id:
+                jg = JobGroup.objects.get(id=self.job_group_id)
+                jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+                output_directory = os.path.join(
+                    pipeline.output_directory, self.ARGOS_NAME, project_prefix, self.ARGOS_VERSION, jg_created_date
+                )
+            helix_filters_outputs_job_data["output_directory"] = output_directory
+        helix_filters_outputs_job = [RunCreator(**helix_filters_outputs_job_data)]
+        return helix_filters_outputs_job
+
+    def add_output_file_names(self, json_data, pipeline_version):
+        """
+        Adds strings that's used by the CWL for output file names
+        """
+        project_prefix = json_data["project_prefix"]
+        json_data["argos_version_string"] = pipeline_version
+        json_data["analysis_mutations_filename"] = project_prefix + ".muts.maf"
+        json_data["analysis_gene_cna_filename"] = project_prefix + ".gene.cna.txt"
+        json_data["analysis_sv_filename"] = project_prefix + ".svs.maf"
+        json_data["analysis_segment_cna_filename"] = project_prefix + ".seg.cna.txt"
+        json_data["cbio_segment_data_filename"] = project_prefix + "_data_cna_hg19.seg"
+        json_data["cbio_meta_cna_segments_filename"] = project_prefix + "_meta_cna_hg19_seg.txt"
+        json_data["analysis_mutations_share_filename"] = project_prefix + ".muts.share.maf"
+        return json_data
+
+    def get_log_directory(self):
+        jg = JobGroup.objects.get(id=self.job_group_id)
+        jg_created_date = jg.created_date.strftime("%Y%m%d_%H_%M_%f")
+        app = self.get_pipeline_id()
+        pipeline = Pipeline.objects.get(id=app)
+        output_directory_prefix = get_project_prefix(self.run_ids)
+        output_directory = os.path.join(
+            pipeline.output_directory,
+            self.ARGOS_NAME,
+            output_directory_prefix,
+            self.ARGOS_VERSION,
+            jg_created_date,
+            "json",
+            pipeline.name,
+            pipeline.version,
+            "%s",
+        )
+        return output_directory
diff --git a/runner/operator/helix_filters/v23_1_0/reference_jsons/helix_filters_resources.json b/runner/operator/helix_filters/v23_1_0/reference_jsons/helix_filters_resources.json
new file mode 100644
index 000000000..770dbb6a4
--- /dev/null
+++ b/runner/operator/helix_filters/v23_1_0/reference_jsons/helix_filters_resources.json
@@ -0,0 +1,69 @@
+{
+    "known_fusions_file": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/known_fusions/known_fusions_at_mskcc.txt",
+    "targets": {
+        "AgilentExon_51MB_b37_v3": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_51MB_b37_v3/b37/AgilentExon_51MB_b37_v3_targets.intervals"
+        },
+        "IDT_Exome_v1_FP_b37": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IDT_Exome_v1_FP/b37/IDT_Exome_v1_FP_b37_targets.ilist"
+        },
+        "E90_NimbleGeneV3_WES": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/E90_NimbleGeneV3_WES/b37/E90_NimbleGeneV3_WES_b37_targets.ilist"
+        },
+        "IMPACT341_b37": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT341/b37/picard_targets.interval_list"
+        },
+        "IMPACT410_b37": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT410/b37/picard_targets.interval_list"
+        },
+        "IMPACT468_b37": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468/b37/picard_targets.interval_list"
+        },
+        "IMPACT468_b37_mm10": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomes/GRCh37_mm10/targets/picard_targets.interval_list"
+        },
+        "IMPACT468_08390": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468_08390/b37/IMPACT468_08390_b37_targets.ilist"
+        },
+        "IMPACT468_08050": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT468_08050/b37/IMPACT468_08050_b37_targets.ilist"
+        },
+        "Agilent_v4_51MB_Human": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/Agilent_v4_51MB_Human/b37/Agilent_v4_51MB_Human_b37_targets.ilist"
+        },
+        "AgilentExon_v2": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_v2/b37/AgilentExon_v2_b37_targets.ilist"
+        },
+        "AgilentExon_v5": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/AgilentExon_v5/b37/AgilentExon_v5_b37_targets.ilist"
+        },
+        "IlluminaExome_38MB": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IlluminaExome_38MB/b37/IlluminaExome_38MB_b37_targets.ilist"
+        },
+        "SeqCap_EZ_Exome_v3": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/SeqCap_EZ_Exome_v3/b37/SeqCap_EZ_Exome_v3_b37_targets.ilist"
+        },
+        "HemePACT_v3_BAIT": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/HemePACT_v3/b37/HemePACT_v3_b37_targets.ilist"
+        },
+        "HemePACT_v4_BAITS": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/HemePACT_v4/b37/HemePACT_v4_b37_targets.ilist"
+        },
+        "IMPACT505_b37": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT505/b37/IMPACT505_b37_targets.ilist"
+        },
+        "IMPACT-Heme_v2_BAITS": {
+            "targets_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/genomic_resources/targets/IMPACT-Heme/IMPACT-Heme_v4/b37/IMPACT-Heme_v4_targets.ilist"
+        }
+    },
+   "IMPACT_gene_list": "juno:///rtsess01/compute/juno/bic/juno/work/ci/helix_filters_01/reference_data/gene_lists/all_IMPACT_genes.tsv",
+   "microsatellites_file": "juno:///rtsess01/compute/juno/bic/juno/work/ci/resources/request_files/msisensor/microsatellites.list",
+   "assay_tmb_coverage_values": {
+        "IMPACT341": 896637,
+        "IMPACT410": 1016335,
+        "IMPACT468": 1139294,
+        "IMPACT505": 1213770,
+        "HemePACT_v4": 1412046,
+        "IMPACT-Heme": 1314920
+   }
+}
\ No newline at end of file
diff --git a/runner/operator/helper.py b/runner/operator/helper.py
index 6c842665d..e52d0f9d4 100644
--- a/runner/operator/helper.py
+++ b/runner/operator/helper.py
@@ -143,3 +143,36 @@ def init_metadata():
     metadata["runId"] = ""
     metadata["preservation"] = ""
     return metadata
+
+
+def pair_samples(fastqs):
+    """
+    pair sample fastqs based on the delivery directory.
+
+    Parameters:
+        fastqs (list): A list of sample fastq files.
+
+    Returns:
+        list: A list of tuples containing paired fastqs for a sample
+    """
+    sample_pairs = []
+    expected_pair = set(["R1", "R2"])
+    # match R1 and R2 based on delivery directory
+    # sorting on file names is not enough as they are non-unique
+    for i, fastq in enumerate(fastqs):
+        dir = "/".join(fastq["path"].split("_R")[0:-1])
+        for compare in fastqs[i + 1 :]:
+            compare_dir = "/".join(compare["path"].split("_R")[0:-1])
+            if dir == compare_dir:
+                # check if R1 and R2 are present
+                r_check = set([fastq["metadata"]["R"], compare["metadata"]["R"]])
+                if r_check.issubset(expected_pair):
+                    # Keep ordering consistent
+                    if fastq["metadata"]["R"] == "R1":
+                        sample_pairs.append((fastq, compare))
+                    else:
+                        sample_pairs.append((compare, fastq))
+                else:
+                    sample_name = fastq["metadata"]["cmoSampleName"]
+                    raise Exception(f"Improper pairing for: {sample_name}")
+    return sample_pairs
diff --git a/runner/operator/manifest/access_manifest_operator.py b/runner/operator/manifest/access_manifest_operator.py
index 94e0c84b3..bbcc69543 100644
--- a/runner/operator/manifest/access_manifest_operator.py
+++ b/runner/operator/manifest/access_manifest_operator.py
@@ -98,18 +98,13 @@ def write_to_file(self, fname, s):
         Writes manifest csv to temporary location, registers it as tmp file
         :return: manifest csv path
         """
-        # Split the string into rows using "\r\n" as the delimiter
-        rows = s.split("\r\n")
-        # Split each row into columns using "," as the delimiter
-        data = [row.split(",") for row in rows]
-        # tmp file creation
+        # output path
         tmpdir = os.path.join(settings.BEAGLE_SHARED_TMPDIR, str(uuid.uuid4()))
         Path(tmpdir).mkdir(parents=True, exist_ok=True)
         output = os.path.join(tmpdir, fname)
         # write csv to tmp file group
-        with open(output, "w+", newline="") as csvfile:
-            writer = csv.writer(csvfile)
-            writer.writerows(data)
+        with open(output, mode="w", encoding="utf-8", newline="") as file:
+            file.write(s)
         # register output as tmp file
         self.register_temp_file(output)
         # return with juno formatting
diff --git a/runner/run/objects/run_creator_object.py b/runner/run/objects/run_creator_object.py
index bcd1136c8..bc411f739 100644
--- a/runner/run/objects/run_creator_object.py
+++ b/runner/run/objects/run_creator_object.py
@@ -64,6 +64,7 @@ def create(self):
         try:
             run.job_group = JobGroup.objects.get(id=self.job_group_id)
         except JobGroup.DoesNotExist:
+            run.job_group = JobGroup.objects.create()
             print("[JobGroup] %s" % self.job_group_id)
         try:
             run.job_group_notifier = JobGroupNotifier.objects.get(id=self.job_group_notifier_id)
diff --git a/runner/tests/operator/access/manifest/test_access_manifest_operator.py b/runner/tests/operator/access/manifest/test_access_manifest_operator.py
index 1154a650e..c17f03daa 100644
--- a/runner/tests/operator/access/manifest/test_access_manifest_operator.py
+++ b/runner/tests/operator/access/manifest/test_access_manifest_operator.py
@@ -7,9 +7,6 @@
 from beagle_etl.models import Operator
 from file_system.models import File, FileMetadata
 from runner.operator.operator_factory import OperatorFactory
-import datetime
-import glob
-import shutil
 
 # general fixtures
 COMMON_FIXTURES = [
@@ -32,10 +29,8 @@
 class TestAcessManifestOperator(TestCase):
     # test db
     fixtures = [os.path.join(ROOT_DIR, f) for f in COMMON_FIXTURES]
-    # variables to help check operator output
-    expected_csv_content = [
-        'igoRequestId,primaryId,cmoPatientId,cmoSampleName,dmpPatientId,dmpImpactSamples,dmpAccessSamples,baitSet,libraryVolume,investigatorSampleId,preservation,species,libraryConcentrationNgul,tissueLocation,sampleClass,sex,cfDNA2dBarcode,sampleOrigin,tubeId,tumorOrNormal,captureConcentrationNm,oncotreeCode,dnaInputNg,collectionYear,captureInputNg\n13893_B,13893_B_1,ALLANT2,C-ALLANT2-N001-d01,P-0000002,P-0000005-T01-IM6;P-0000004-T01-IM6,,MSK-ACCESS-v1_0-probesAllwFP,25.0,P-1234567-N00-XS1,EDTA-Streck,,69.0,,Blood,M,8042889270,Whole Blood,,Normal,14.49275362,,200.0,,999.99999978\n13893_B,13893_B_3,ALLANT,C-ALLANT-N001-d01,P-0000001,P-0000002-T01-IM6;P-0000001-T01-IM6,,MSK-ACCESS-v1_0-probesAllwFP,25.0,P-1234567-N00-XS1,EDTA-Streck,,102.5,,Blood,M,8042889270,Whole Blood,,Normal,9.756097561,,200.0,,1000.0000000025001\n13893_B,13893_B_2,ALLANT3,C-ALLANT3-N003-d02,,,,MSK-ACCESS-v1_0-probesAllwFP,25.0,P-1234567-N00-XS1,EDTA-Streck,,74.5,,Blood,M,8042889270,Whole Blood,,Normal,13.42281879,,200.0,,999.999999855\n""\n'
-    ]
+    header_control = "igoRequestId,primaryId,cmoPatientId,cmoSampleName,dmpPatientId,dmpImpactSamples,dmpAccessSamples,baitSet,libraryVolume,investigatorSampleId,preservation,species,libraryConcentrationNgul,tissueLocation,sampleClass,sex,cfDNA2dBarcode,sampleOrigin,tubeId,tumorOrNormal,captureConcentrationNm,oncotreeCode,dnaInputNg,collectionYear,captureInputNg"
+    id_control = "C-ALLANT-N001-d01"
 
     def test_access_manifest_operator(self):
         """
@@ -60,6 +55,9 @@ def test_access_manifest_operator(self):
             self.assertEqual(len(input_json["manifest_data"]), 2)
             # Check contents
             manifest_path = input_json["manifest_data"]["location"].replace("juno:", "")
-            with open(manifest_path, "r") as file:
-                csv_string = file.read()
-            self.assertEqual(csv_string, self.expected_csv_content[i])
+            with open(manifest_path, mode="r", newline="", encoding="utf-8") as file:
+                content = file.read()
+            header = content.split("\r\n")[0]
+            id = content.split("\r\n")[1].split(",")[3]
+            self.assertEqual(header, self.header_control)
+            self.assertEqual(id, self.id_control)
diff --git a/runner/tests/operator/argos_bam_operator/v1_0_0/__init__.py b/runner/tests/operator/argos_bam_operator/v1_0_0/__init__.py
index b213612fc..e69de29bb 100644
--- a/runner/tests/operator/argos_bam_operator/v1_0_0/__init__.py
+++ b/runner/tests/operator/argos_bam_operator/v1_0_0/__init__.py
@@ -1 +0,0 @@
-from runner.operator.argos_bam_operator.v1_0_0 import ArgosBamOperator
diff --git a/runner/tests/operator/argos_bam_operator/v1_1_0/__init__.py b/runner/tests/operator/argos_bam_operator/v1_1_0/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/runner/tests/operator/argos_bam_operator/v1_1_0/test_construct_argos_pair.py b/runner/tests/operator/argos_bam_operator/v1_1_0/test_construct_argos_pair.py
new file mode 100644
index 000000000..12ac6c6a0
--- /dev/null
+++ b/runner/tests/operator/argos_bam_operator/v1_1_0/test_construct_argos_pair.py
@@ -0,0 +1,106 @@
+"""
+Test for constructing Argos pair and jobs
+"""
+
+import os
+import json
+from django.test import TestCase
+from django.conf import settings
+from django.core.management import call_command
+from file_system.models import File
+from runner.operator.argos_bam_operator.v1_1_0.bin.make_sample import build_sample
+from runner.operator.argos_bam_operator.v1_1_0.construct_argos_pair import construct_argos_jobs
+
+
+class TestConstructPair(TestCase):
+    # load fixtures for the test case temp db
+    fixtures = ["file_system.filegroup.json", "file_system.filetype.json", "file_system.storage.json"]
+
+    def setUp(self):
+        os.environ["TMPDIR"] = ""
+
+    def test_construct_argos_jobs1(self):
+        """
+        Test that Argos jobs are correctly created
+        """
+        # Load fixtures
+        test_files_fixture = os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.file.json")
+        call_command("loaddata", test_files_fixture, verbosity=0)
+        test_files_fixture = os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.filemetadata.json")
+        call_command("loaddata", test_files_fixture, verbosity=0)
+
+        request_id_metadata_key = settings.REQUEST_ID_METADATA_KEY
+        igo_complete_metadata_key = settings.IGO_COMPLETE_METADATA_KEY
+
+        files = File.objects.filter(
+            **{
+                "filemetadata__metadata__{}".format(request_id_metadata_key): "10075_D",
+                "filemetadata__metadata__{}".format(igo_complete_metadata_key): True,
+            }
+        ).all()
+        data = list()
+        for file in files:
+            sample = dict()
+            sample["id"] = file.id
+            sample["path"] = file.path
+            sample["file_name"] = file.file_name
+            sample["metadata"] = file.filemetadata_set.first().metadata
+            data.append(sample)
+        samples = list()
+        # group by igoId
+        igo_id_group = dict()
+        for sample in data:
+            igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+            if igo_id not in igo_id_group:
+                igo_id_group[igo_id] = list()
+            igo_id_group[igo_id].append(sample)
+
+        for igo_id in igo_id_group:
+            samples.append(build_sample(igo_id_group[igo_id]))
+
+        argos_inputs, error_samples = construct_argos_jobs(samples)
+        expected_inputs = json.load(
+            open(os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.argos_bam.input.json"))
+        )
+        files.delete()
+        print("Running test_construct_argos_jobs1")
+        self.assertTrue(argos_inputs == expected_inputs)
+
+    def test_construct_argos_jobs_pdx(self):
+        test_files_fixture = os.path.join(settings.TEST_FIXTURE_DIR, "08944_B.fixtures.json")
+        call_command("loaddata", test_files_fixture, verbosity=0)
+
+        request_id_metadata_key = settings.REQUEST_ID_METADATA_KEY
+        igo_complete_metadata_key = settings.IGO_COMPLETE_METADATA_KEY
+
+        files = File.objects.filter(
+            **{
+                "filemetadata__metadata__{}".format(request_id_metadata_key): "08944_B",
+                "filemetadata__metadata__{}".format(igo_complete_metadata_key): True,
+            }
+        ).all()
+        data = list()
+        for file in files:
+            sample = dict()
+            sample["id"] = file.id
+            sample["path"] = file.path
+            sample["file_name"] = file.file_name
+            sample["metadata"] = file.filemetadata_set.first().metadata
+            data.append(sample)
+        samples = list()
+
+        igo_id_group = dict()
+        for sample in data:
+            igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+            if igo_id not in igo_id_group:
+                igo_id_group[igo_id] = list()
+            igo_id_group[igo_id].append(sample)
+
+        for igo_id in igo_id_group:
+            samples.append(build_sample(igo_id_group[igo_id]))
+
+        argos_inputs, error_samples = construct_argos_jobs(samples)
+        expected_inputs = json.load(open(os.path.join(settings.TEST_FIXTURE_DIR, "08944_B.argos_bam.input.json")))
+        print(json.dumps(argos_inputs))
+        print("Running test_construct_argos_jobs_pdx")
+        self.assertTrue(argos_inputs == expected_inputs)
diff --git a/runner/tests/operator/argos_operator/v1_2_0/test_construct_argos_pair.py b/runner/tests/operator/argos_operator/v1_2_0/test_construct_argos_pair.py
index d849a88f4..44f53d247 100644
--- a/runner/tests/operator/argos_operator/v1_2_0/test_construct_argos_pair.py
+++ b/runner/tests/operator/argos_operator/v1_2_0/test_construct_argos_pair.py
@@ -3,15 +3,12 @@
 """
 import os
 import json
-from deepdiff import DeepDiff
-from pprint import pprint
-from uuid import UUID
 from django.test import TestCase
-from runner.operator.argos_operator.v1_2_0.construct_argos_pair import construct_argos_jobs
-from runner.operator.argos_operator.v1_2_0.bin.make_sample import build_sample
-from file_system.models import File, FileMetadata, FileGroup, FileType
 from django.conf import settings
 from django.core.management import call_command
+from file_system.models import File
+from runner.operator.argos_operator.v1_2_0.bin.make_sample import build_sample
+from runner.operator.argos_operator.v1_2_0.construct_argos_pair import construct_argos_jobs
 
 
 class TestConstructPair(TestCase):
@@ -64,6 +61,5 @@ def test_construct_argos_jobs1(self):
         expected_inputs = json.load(
             open(os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.argos.input.json"))
         )
-
         print("Running test_construct_argos_jobs1")
         self.assertTrue(argos_inputs == expected_inputs)
diff --git a/runner/tests/operator/argos_operator/v2_1_0/__init__.py b/runner/tests/operator/argos_operator/v2_1_0/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/runner/tests/operator/argos_operator/v2_1_0/test_construct_argos_pair.py b/runner/tests/operator/argos_operator/v2_1_0/test_construct_argos_pair.py
new file mode 100644
index 000000000..c6df0f9b8
--- /dev/null
+++ b/runner/tests/operator/argos_operator/v2_1_0/test_construct_argos_pair.py
@@ -0,0 +1,104 @@
+"""
+Test for constructing Argos pair and jobs
+"""
+import os
+import json
+from django.test import TestCase
+from django.conf import settings
+from django.core.management import call_command
+from file_system.models import File
+from runner.operator.argos_operator.v2_1_0.bin.make_sample import build_sample
+from runner.operator.argos_operator.v2_1_0.construct_argos_pair import construct_argos_jobs
+
+
+class TestConstructPair(TestCase):
+    # load fixtures for the test case temp db
+    fixtures = ["file_system.filegroup.json", "file_system.filetype.json", "file_system.storage.json"]
+
+    def setUp(self):
+        os.environ["TMPDIR"] = ""
+
+    def test_construct_argos_jobs1(self):
+        """
+        Test that Argos jobs are correctly created
+        """
+        # Load fixtures
+        test_files_fixture = os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.file.json")
+        call_command("loaddata", test_files_fixture, verbosity=0)
+        test_files_fixture = os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.filemetadata.json")
+        call_command("loaddata", test_files_fixture, verbosity=0)
+
+        request_id_metadata_key = settings.REQUEST_ID_METADATA_KEY
+        igo_complete_metadata_key = settings.IGO_COMPLETE_METADATA_KEY
+
+        files = File.objects.filter(
+            **{
+                "filemetadata__metadata__{}".format(request_id_metadata_key): "10075_D",
+                "filemetadata__metadata__{}".format(igo_complete_metadata_key): True,
+            }
+        ).all()
+        data = list()
+        for file in files:
+            sample = dict()
+            sample["id"] = file.id
+            sample["path"] = file.path
+            sample["file_name"] = file.file_name
+            sample["metadata"] = file.filemetadata_set.first().metadata
+            data.append(sample)
+        samples = list()
+        # group by igoId
+        igo_id_group = dict()
+        for sample in data:
+            igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+            if igo_id not in igo_id_group:
+                igo_id_group[igo_id] = list()
+            igo_id_group[igo_id].append(sample)
+
+        for igo_id in igo_id_group:
+            samples.append(build_sample(igo_id_group[igo_id]))
+
+        argos_inputs, error_samples = construct_argos_jobs(samples)
+        expected_inputs = json.load(
+            open(os.path.join(settings.TEST_FIXTURE_DIR, "10075_D_single_TN_pair.argos_1_7_0.input.json"))
+        )
+
+        print("Running test_construct_argos_jobs1")
+        self.assertTrue(argos_inputs == expected_inputs)
+
+    def test_construct_argos_jobs_pdx(self):
+        test_files_fixture = os.path.join(settings.TEST_FIXTURE_DIR, "08944_B.fixtures.json")
+        call_command("loaddata", test_files_fixture, verbosity=0)
+
+        request_id_metadata_key = settings.REQUEST_ID_METADATA_KEY
+        igo_complete_metadata_key = settings.IGO_COMPLETE_METADATA_KEY
+
+        files = File.objects.filter(
+            **{
+                "filemetadata__metadata__{}".format(request_id_metadata_key): "08944_B",
+                "filemetadata__metadata__{}".format(igo_complete_metadata_key): True,
+            }
+        ).all()
+        data = list()
+        for file in files:
+            sample = dict()
+            sample["id"] = file.id
+            sample["path"] = file.path
+            sample["file_name"] = file.file_name
+            sample["metadata"] = file.filemetadata_set.first().metadata
+            data.append(sample)
+        samples = list()
+
+        igo_id_group = dict()
+        for sample in data:
+            igo_id = sample["metadata"][settings.SAMPLE_ID_METADATA_KEY]
+            if igo_id not in igo_id_group:
+                igo_id_group[igo_id] = list()
+            igo_id_group[igo_id].append(sample)
+
+        for igo_id in igo_id_group:
+            samples.append(build_sample(igo_id_group[igo_id]))
+
+        argos_inputs, error_samples = construct_argos_jobs(samples)
+        expected_inputs = json.load(open(os.path.join(settings.TEST_FIXTURE_DIR, "08944_B.argos.input.json")))
+        print("Running test_construct_argos_jobs_pdx")
+        self.assertTrue(argos_inputs == expected_inputs)
diff --git a/runner/tests/operator/copy_outputs_operator/v1_2_0/__init__.py b/runner/tests/operator/copy_outputs_operator/v1_2_0/__init__.py
index 34824ecaf..598eff9e3 100644
--- a/runner/tests/operator/copy_outputs_operator/v1_2_0/__init__.py
+++ b/runner/tests/operator/copy_outputs_operator/v1_2_0/__init__.py
@@ -1 +1 @@
-from runner.operator.copy_outputs_operator.v1_0_0 import CopyOutputsOperator
+from runner.operator.copy_outputs_operator.v1_2_0 import CopyOutputsOperator
diff --git a/runner/tests/operator/copy_outputs_operator/v2_1_0/__init__.py b/runner/tests/operator/copy_outputs_operator/v2_1_0/__init__.py
new file mode 100644
index 000000000..73ad9399c
--- /dev/null
+++ b/runner/tests/operator/copy_outputs_operator/v2_1_0/__init__.py
@@ -0,0 +1 @@
+from runner.operator.copy_outputs_operator.v2_1_0 import CopyOutputsOperator
diff --git a/runner/tests/operator/copy_outputs_operator/v2_1_0/test_copy_outputs_operator.py b/runner/tests/operator/copy_outputs_operator/v2_1_0/test_copy_outputs_operator.py
new file mode 100644
index 000000000..8ea58f31a
--- /dev/null
+++ b/runner/tests/operator/copy_outputs_operator/v2_1_0/test_copy_outputs_operator.py
@@ -0,0 +1,124 @@
+"""
+Test for constructing Argos pair and jobs
+"""
+
+import os
+import json
+from uuid import UUID
+from mock import patch
+from django.test import TestCase
+from runner.operator.operator_factory import OperatorFactory
+from beagle_etl.models import Operator
+from django.conf import settings
+from django.core.management import call_command
+import tempfile
+
+
+class TestCopyOutputs(TestCase):
+    # load fixtures for the test case temp db
+    fixtures = [
+        "file_system.filegroup.json",
+        "file_system.filetype.json",
+        "file_system.storage.json",
+        "beagle_etl.operator.json",
+        "runner.pipeline.json",
+    ]
+
+    def setUp(self):
+        os.environ["TMPDIR"] = ""
+
+    def check_if_file_obj_valid(self, file_obj):
+        if "class" in file_obj and "location" in file_obj:
+            if file_obj["class"] == "File" and type(file_obj["location"]) == str:
+                return True
+        return False
+
+    def check_if_list_is_valid(self, object_list, empty_ok, validation_function):
+        if not empty_ok:
+            if len(object_list) == 0:
+                return False
+        for single_obj in object_list:
+            if not validation_function(single_obj):
+                return False
+        return True
+
+    def validate_copy_outputs_input(self, input_json):
+        for single_field in input_json:
+            if single_field == "project_prefix":
+                continue  # test is failing for this value because project_prefix isn't actually a list
+            elif single_field == "meta":
+                if not len(input_json[single_field]) == 3:
+                    return False
+                else:
+                    continue
+            elif single_field == "disambiguate":
+                if not self.check_if_list_is_valid(input_json[single_field], True, self.check_if_file_obj_valid):
+                    print("Error at %s", single_field)
+                    return False
+            else:
+                if not self.check_if_list_is_valid(input_json[single_field], False, self.check_if_file_obj_valid):
+                    print("Error at %s", single_field)
+                    return False
+        return True
+
+    @patch("file_system.models.populate_job_group_notifier_metadata.delay")
+    def test_create_copy_output_jobs(self, populate_job_group_notifier_metadata):
+        """
+        Test that copy output jobs are correctly created
+        """
+        print("Running test_create_copy_output_jobs ----")
+        # Load fixtures
+        populate_job_group_notifier_metadata.return_value = None
+        test_files_fixture = os.path.join(
+            settings.TEST_FIXTURE_DIR, "ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.with_disambiguate.json"
+        )
+        call_command("loaddata", test_files_fixture, verbosity=0)
+
+        with self.settings(BEAGLE_SHARED_TMPDIR=tempfile.gettempdir()):
+            operator_model = Operator.objects.get(id=25)
+            operator = OperatorFactory.get_by_model(
+                operator_model, version="v2.1.0", run_ids=["ca18b090-03ad-4bef-acd3-52600f8e62eb"]
+            )
+            input_json_valid = False
+            jobs = operator.get_jobs()
+            if jobs[0].is_valid():
+                input_json = jobs[0].inputs
+                self.assertEqual(len(input_json["disambiguate"]), 4)
+                input_json_valid = self.validate_copy_outputs_input(input_json)
+                print(json.dumps(input_json, cls=UUIDEncoder))
+            self.assertEqual(input_json_valid, True)
+
+    @patch("file_system.models.populate_job_group_notifier_metadata.delay")
+    def test_create_copy_output_jobs_without_disambiguate(self, populate_job_group_notifier_metadata):
+        """
+        Test that copy output jobs are correctly created
+        """
+        print("Running test_create_copy_output_jobs ----")
+        populate_job_group_notifier_metadata.return_value = None
+        # Load fixtures
+        test_files_fixture = os.path.join(
+            settings.TEST_FIXTURE_DIR, "ca18b090-03ad-4bef-acd3-52600f8e62eb.run.full.without_disambiguate.json"
+        )
+        call_command("loaddata", test_files_fixture, verbosity=0)
+
+        with self.settings(BEAGLE_SHARED_TMPDIR=tempfile.gettempdir()):
+            operator_model = Operator.objects.get(id=25)
+            operator = OperatorFactory.get_by_model(
+                operator_model, version="v2.1.0", run_ids=["ca18b090-03ad-4bef-acd3-52600f8e62eb"]
+            )
+            input_json_valid = False
+            jobs = operator.get_jobs()
+            if jobs[0].is_valid():
+                input_json = jobs[0].inputs
+                self.assertEqual(len(input_json["disambiguate"]), 0)
+                input_json_valid = self.validate_copy_outputs_input(input_json)
+                print(json.dumps(input_json, cls=UUIDEncoder))
+            self.assertEqual(input_json_valid, True)
+
+
+class UUIDEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, UUID):
+            # if the obj is uuid, we simply return the value of uuid
+            return obj.hex
+        return json.JSONEncoder.default(self, obj)