Chienlab-TNseq

Introduction

chienlab-tnseq is a Snakemake pipeline for performing bacterial TNseq alignment and annotation.

Pipeline summary

The pipeline will perform the following steps:

1. Removes reads that does not contain the static transposon region. (Seqkit)
2. Removes PCR duplicates using UMIs. (JE)
3. Align reads to reference genome. (BWA-MEM)
4. Generates BigWig files for visualization in genome browsers (deeptools)
5. Assigns 5' position to counts. (bedtools genomecov)
6. Maps position counts to gene features using either clipped or full sized genes. (bedtools map)
7. Creates tab-separated count files.

Installation and basic usage

You will need to install Conda package manager.

1. Clone the repository

git clone https://github.com/baldikacti/chienlab-tnseq.git

2. Change your working directory to cloned repository
3. Create and activate the conda environment

conda env create -f envs/environment.yaml
conda activate chienlab-tnseq

4. Open config/config.yaml in your favorite text editor.

Change the fastq: "data/test/" paramater to the directory that contains your fastq files

Change the results: "results/test/" paramater to the directory that you want the results to be exported to.

It is important to keep the forward slashed at the end of directory paths.

The config file contains parameters for fasta and bed formatted gene feature references. Change those as needed for different organisms.

5. Run the pipeline with select number of cores.

snakemake --use-conda --cores 8

Outputs

1. preprocess directory containing filtered fastq files based on the transposon sequence
2. bwa_aln directory containing BAM files.
3. bigwig directory containing BigWig files.
4. mapped directory containing mapped bed formatted files
5. read_counts directory containing:
   1. totalcounts_mid: total count files that are mapped to clipped gene features.
   2. totalcounts_full: total count files that are mapped to full sized gene features.
   3. uniquecounts_mid: unique count files that are mapped to clipped gene features.
   4. uniquecounts_full: unique count files that are mapped to full sized gene features.