Map population to reference genome

First follow the instructions here:

Step by step guide on how to use my pipelines
Click here for an introduction to Snakemake

Create conda environmet:

conda env create --name <name-of-pipeline> --file population-variant-calling.yml

This environment contains snakemake and the other packages that are needed to run the pipeline.

Activate environmet:

conda activate <name-of-pipeline>

Create HPC config file:

Necessary for snakemake to prepare and send jobs.

mkdir -p ~/.config/snakemake/<name-of-pipeline>
nano ~/.config/snakemake/<name-of-pipeline>/config.yaml

Include the following and save the file (ctr+x)

jobs: 10
cluster: "sbatch -t 2-0:0:0 --mem=16000 -c8 --job-name={rule} --output=logs_slurm/{rule}_%j.out --error=logs_slurm/{rule}_%j.err"

use-conda: true

ABOUT

This is a pipeline to map short reads from several individuals to a reference assembly. It outputs the mapped reads and a qualimap report.

Tools used:

• Bwa-mem2 - mapping
• Samtools - processing
• Samblaster - marking duplicates
• Qualimap - mapping summary

Pipeline workflow

Edit config.yaml with the paths to your files

ASSEMBLY: /path/to/assembly
OUTDIR: /path/to/outdir
PATHS_WITH_FILES:
  path1: /path/to/dir

• ASSEMBLY - path to the assembly file
• OUTDIR - directory where snakemake will run and where the results will be written to.
  If you want the results to be written to this directory (not to a new directory), comment out OUTDIR: /path/to/outdir
• PATHS_WITH_FILES - directory that can contain subdirectories where the fastq reads are located. You can add several paths by adding path2: /path/to/dir under PATHS_WITH_FILES. (The line you add has to have indentation)

The script goes through the subdirectories of the directory you choose under PATHS_WITH_FILES looking for files with fastq extension.
Example: if path1: /lustre/nobackup/WUR/ABGC/shared/Chicken/Africa/X201SC20031230-Z01-F006_multipath, the subdirectory structure could be:

/lustre/nobackup/WUR/ABGC/shared/Chicken/Africa/X201SC20031230-Z01-F006_multipath  
├── X201SC20031230-Z01-F006_1  
│   └── raw_data  
│       ├── a109_26_15_1_H  
│       │   ├── a109_26_15_1_H_FDSW202597655-1r_HWFFFDSXY_L3_1.fq.gz  
│       │   ├── a109_26_15_1_H_FDSW202597655-1r_HWFFFDSXY_L3_2.fq.gz  
│       │   └── MD5.txt  
│       └── a20_10_16_1_H  
│           ├── a20_10_16_1_H_FDSW202597566-1r_HWFFFDSXY_L3_1.fq.gz  
│           ├── a20_10_16_1_H_FDSW202597566-1r_HWFFFDSXY_L3_2.fq.gz  
│           └── MD5.txt  
└── X201SC20031230-Z01-F006_2  
    └── raw_data  
        ├── a349_Be_17_1_C  
        │   ├── a349_Be_17_1_C_FDSW202597895-1r_HWFFFDSXY_L3_1.fq.gz  
        │   ├── a349_Be_17_1_C_FDSW202597895-1r_HWFFFDSXY_L3_2.fq.gz  
        │   └── MD5.txt  
        └── a360_Be_05_1_H  
            ├── a360_Be_05_1_H_FDSW202597906-1r_HWFFFDSXY_L3_1.fq.gz  
            ├── a360_Be_05_1_H_FDSW202597906-1r_HWFFFDSXY_L3_2.fq.gz  
            └── MD5.txt  

RESULTS

• <run_date>_files.txt dated file with an overview of the files used to run the pipeline (for documentation purposes)
• processed_reads directory with the bam files with the mapped reads for every sample
• mapping_stats directory containing the qualimap results and a summary of the qualimap results for all samples in sample_quality_summary.tsv
  • qualimap contains qualimap results per sample