Exosome variant analysis

This guide provides a step-by-step guide to 1) convert BAM files (i.e., public) to FASTQ; and 2) run the nextflow nf-core/sarek variant calling pipeline.

Date created: 19/01/2023; Last update: 20/01/2023

Create a Conda environment with tools needed for downstream analyze

Create a python 3.7 environment:

conda create --name liver python=3.7

Activate the conda environment:

conda activate liver

Prepare a file called environment.yml - Tip: use a text editor (i.e., vim, nano, or other) to copy and paste the code below into the file.

channels:
  - bioconda
  - conda-forge
dependencies:
  - bedtools
  - samtools
  - seqkit
  - vcftools
  - emboss

Run the following command to install additional tools

conda env update --file environment.yml

To deactivate the conda environment, run:

conda deactivate

Convert BAM to FASTQ

Move to the folder where all the BAM files are present and prepare the following script (i.e., launch_BAM2FASTQ.pbs):

#!/bin/bash -l
#PBS -N BAM2FASTQ
#PBS -l walltime=24:00:00
#PBS -l mem=8gb
#PBS -l ncpus=4

cd $PBS_O_WORKDIR

#activate the conda environment with the necessary tools
conda activate liver

#Sort reads in BAM file by indentifier-name (-n) using 4 CPUs (-@ 4). Note 'prefix' for sorted file noted after $i (input BAM file)
for i in `ls --color=never *.bam`
do
  echo $i
  samtools sort -@ 4 -n $i ${i%%.bam}_sorted
done

#Extract paired end reads in FASTQ format
for file in `ls --color=never *sorted.bam`
do
  echo $file
  bedtools bamtofastq -i $file -fq ${file%%.bam}_R1.fastq -fq2 ${file%%.bam}_R2.fastq
  #compress FASTQ files to run using the sarek pipeline
  gzip -c -9 ${file%%.bam}_R1.fastq > ${file%%.bam}_R1.fastq.gz
  gzip -c -9 ${file%%.bam}_R1.fastq > ${file%%.bam}_R2.fastq.gz
done

Submit the job to the PBS scehduler:

qsub launch_BAM2FASTQ.pbs

Check the submitted job(s):

qjobs

Run variant calling using the nextflow nf-core/sarek pipeline

To run Sarek 3 files are required:

launch.pbs → details how to run the workflow
nextflow.config → specify how to run the workflow in the HPC
samplesheet.csv → provides information on the samples and data to be used (i.e., FASTQ, BAM or CRAM)

Below is an example of a launch.pbs file:

#!/bin/bash -l
#PBS -N sarek
#PBS -l walltime=24:00:00
#PBS -l select=1:ncpus=1:mem=5gb
cd $PBS_O_WORKDIR
NXF_OPTS='-Xms1g -Xmx4g'
module load java

nextflow run nf-core/sarek \
        -r 3.1.1 \
        -profile singularity \
        --genome GATK.GRCh38 \
        --input index.csv \
        -config nextflow.config

nextflow.config file:

singularity {
    cacheDir = '$HOME/NXF_SINGULARITY_CACHEDIR'
    autoMounts = true
}

conda {
    cacheDir = '$HOME/NXF_CONDA_CACHEDIR'
}

singularity {
    enabled = true
    autoMounts = true
}

process {
  executor = 'pbspro'
  beforeScript = {
      """
      source $HOME/.bashrc
      source $HOME/.profile
      """
  }
  scratch = false
  cleanup = false
}

Example of an samplesheet.csv file:

patient,sample,lane,fastq_1,fastq_2
healthy_11,1,1,/path/to/data/NAFLD_exome_sequencing-166537376/1.Healthy/rename/Healthy_Combined_11_sorted_R1.fastq.gz,/path/to/data/NAFLD_exome_sequencing-166537376/1.Healthy/rename/Healthy_Combined_11_sorted_R2.fastq.gz