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# Created by https://www.gitignore.io/api/go,node,code,macos,intellij+iml | ||
# Edit at https://www.gitignore.io/?templates=go,node,code,macos,intellij+iml | ||
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### Code ### | ||
.vscode/* | ||
!.vscode/settings.json | ||
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### Go ### | ||
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*.exe | ||
*.exe~ | ||
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# Test binary, built with `go test -c` | ||
*.test | ||
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# Output of the go coverage tool, specifically when used with LiteIDE | ||
*.out | ||
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### Intellij+iml ### | ||
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm | ||
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 | ||
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# User-specific stuff | ||
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### Intellij+iml Patch ### | ||
# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023 | ||
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services/verdict-web/dist | ||
import-tool/input/omim/morbidmap.txt | ||
import-tool/input/omim/mimTitles.txt | ||
import-tool/input/omim/mim2gene.txt | ||
import-tool/input/hugo/genes-with-ncbi-mapping.txt | ||
import-tool/input/hprd/BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt | ||
import-tool/input/cardigan/weighted_predictions.txt | ||
import-tool/input/caniza/combined_similarity_triplet.tsv |
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# VerDICT - Visualisation of Disease-Gene Predictions in Context | ||
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According to the recently developed paradigm of Network Medicine, hereditary diseases can be thought of as perturbations in the transmission of signals on networks of interacting biomolecules, known as the interactome. Several algorithms have recently been proposed that exploit the principles of Network medicine to predict disease genes for hereditary diseases. However, no tool currently exists that allows the clinician scientists to visualise the predicted disease genes in the context of the interactome. This project aims at filling this gap, by developing VerDICT, an intuitive, user-friendly environment where clinician-scientists can reason about the predictions generated by the algorithms and generate a hypothesis to test experimentally. The program allows the users to include in their analysis of metabolic pathways, information about related medical conditions as well as user-specific domain knowledge in the form of custom groups of genes. VerDICT is highly customizable, it integrates data from the most relevant disease-related database, OMIM, Kegg and Entrez, and is freely available to the scientific community as a web application from the [PaccanaroLab website](https://www.paccanarolab.org/VerDICT). | ||
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# Installation | ||
This installation requires a Kubernetes cluster. Instructions for installation will be added soon. | ||
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# Testing | ||
Instructions for testing will be added soon. |
Submodule deployment
added at
e9966c
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# CARDIGAN | ||
# cd cardigan; tail -n+2 dt2.txt| python ./split-to-lines.py > ../nov15/disease-cardigan-gene.txt; cd .. | ||
# import sys | ||
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# for line in sys.stdin: | ||
# disease = str(line).split('\t')[0] | ||
# prots = str(line).split('\t')[2].split('|') | ||
# for protein in prots: | ||
# print(disease.rstrip() + '|' + protein.rstrip() + '|PREDICTED_INTERACTION') | ||
# pass | ||
# pass | ||
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# cut -d'|' -f1 diseases.nodes.txt | python build-entrez-morbidmap.py > ../nov15/disease-morbidmap-gene.txt | ||
# import sys | ||
# from collections import defaultdict | ||
# | ||
# FORMATTED_MORBID_MAP_FILE = './morbidmap-omimdisease-omimgene.txt' | ||
# MIM_TO_GENE = './mim2gene.txt' | ||
# | ||
# # | ||
# # Expects OMIM disease identifiers from stdin which are then mapped to `morbidmap.txt` | ||
# # where the entrez ID is found from mim2gene | ||
# # | ||
# | ||
# mimtogene = {} | ||
# | ||
# with open(MIM_TO_GENE, "rb") as mimToGenePSV: | ||
# for line in mimToGenePSV: | ||
# fields = line.split('\t') | ||
# if(len(fields) > 1 | ||
# and fields[1] == 'gene' | ||
# and fields[2] != ''): | ||
# mimtogene[fields[0].rstrip()] = fields[2].rstrip() | ||
# mimToGenePSV.close() | ||
# | ||
# morbidmap = defaultdict(list) | ||
# | ||
# with open(FORMATTED_MORBID_MAP_FILE, "rb") as morbidmapPSV: | ||
# for line in morbidmapPSV: | ||
# fields = line.split('|') | ||
# if(len(fields) > 1 and fields[1].rstrip() in mimtogene): | ||
# morbidmap[fields[0]].append(mimtogene[fields[1].rstrip()]) | ||
# morbidmapPSV.close() | ||
# | ||
# for diseaseomim in sys.stdin: | ||
# formattedDiseaseOmim = str(diseaseomim).rstrip() | ||
# for relatedgenes in morbidmap[formattedDiseaseOmim]: | ||
# print(formattedDiseaseOmim+ '|' + relatedgenes + '|'+'HAS_GENE') | ||
# pass | ||
# pass | ||
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from pathlib import Path | ||
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from lib.DiseaseSimilarity import DiseaseSimilarity | ||
from lib.Hugo import Hugo | ||
from lib.OMIM import OMIM | ||
from lib.FileUtilities import FileUtilities | ||
from lib.Cardigan import Cardigan | ||
from lib.PPI import PPI | ||
from lib.HPRD import HPRD | ||
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OUTPUT_DIR = Path('output') | ||
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OMIM_MIMTITLES = Path('input/omim/mimTitles.txt') | ||
OMIM_MORBIDMAP = Path('input/omim/morbidmap.txt') | ||
OMIM_MIM2GENE = Path('input/omim/mim2gene.txt') | ||
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CARDIGAN_PREDICTIONS = Path('input/cardigan/weighted_predictions.txt') | ||
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BIOGRID_PPI = Path('input/biogrid/ppi.txt') | ||
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HUGO_GENES = Path('input/hugo/genes-with-ncbi-mapping.txt') | ||
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HPRD_PPI = Path('input/hprd/BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt') | ||
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CANIZA_SIMILARITY = Path('input/caniza/combined_similarity_triplet.tsv') | ||
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abnormal_omim_entries = [616902] | ||
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if __name__ == '__main__': | ||
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fileUtils = FileUtilities() | ||
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hugo = Hugo(HUGO_GENES) | ||
omim = OMIM(OMIM_MIMTITLES, OMIM_MORBIDMAP, OMIM_MIM2GENE) | ||
cardigan = Cardigan(CARDIGAN_PREDICTIONS) | ||
# biogrid = PPI(BIOGRID_PPI) | ||
disease_sim = DiseaseSimilarity(CANIZA_SIMILARITY) | ||
hprd = HPRD(HUGO_GENES, HPRD_PPI) | ||
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fileUtils.write_nodes(hugo.get_genes(), OUTPUT_DIR.joinpath('gene.nodes.txt').open('w+')) | ||
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fileUtils.write_nodes(omim.get_phenotypes(), OUTPUT_DIR.joinpath('disease.nodes.txt').open('w+')) | ||
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fileUtils.json_write_nodes(omim.get_phenotypes(), OUTPUT_DIR.joinpath('disease.nodes.json').open('w+')) | ||
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fileUtils.json_write_nodes(hugo.get_genes(), OUTPUT_DIR.joinpath('gene.nodes.json').open('w+')) | ||
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fileUtils.write_relationships(omim.get_morbidmap_associations(), OUTPUT_DIR.joinpath('disease-morbidmap-gene.txt').open('w+')) | ||
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fileUtils.write_relationships(cardigan.get_predictions(), OUTPUT_DIR.joinpath('disease-cardigan-gene.txt').open('w+')) | ||
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# fileUtils.write_relationships(biogrid.get_values(), OUTPUT_DIR.joinpath('gene-interacts-gene.txt').open('w+')) | ||
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fileUtils.write_relationships(hprd.get_hprd(), OUTPUT_DIR.joinpath('gene-interacts-gene.txt').open('w+')) | ||
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fileUtils.write_relationships(disease_sim.get_similarity(), OUTPUT_DIR.joinpath('disease-sim-disease.txt').open('w+')) |
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#!/bin/sh | ||
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neo4j-admin import --nodes:PHENOTYPE /var/lib/neo4j/import/output/disease.nodes.txt --nodes:GENE /var/lib/neo4j/import/output/gene.nodes.txt --relationships /var/lib/neo4j/import/output/disease-cardigan-gene.txt --relationships /var/lib/neo4j/import/output/disease-morbidmap-gene.txt --relationships /var/lib/neo4j/import/output/disease-sim-disease.txt --relationships /var/lib/neo4j/import/output/gene-interacts-gene.txt --id-type=INTEGER --delimiter="|" --ignore-duplicate-nodes --ignore-missing-nodes | ||
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exec /var/lib/neo4j/bin/neo4j console |
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