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Nextflow pipeline for calling SNPs based on GATK best practices

Table of contents

  1. Overview
  2. Installation
  3. Running the pipeline
  4. Dependencies
  5. Credits
  6. Citation

Overview

Here is a high-level summary of the pipeline:

  • Convert BAM to fastq.
  • Fastqc on the input files
  • Trim Galore! on the input files to trim reads and repeat quality control on trimmed reads.
  • Align reads to a reference genome with BWA-MEM
  • Sort, index and return statistics with samtools
  • Remove duplicate reads with picard
  • qualimap on deduplicated reads
  • Not run: GATK to realign the reads at the positions where there are indels (this was deprecated in GATK 4)
  • Base recalibration with GATK tools BaseRecalibrator, ApplyBQSR and AnalyzeCovariates
  • SNP calls with gatk HaplotypeCaller.
  • Multiqc to summarise the various QC checks.

See main.nf for full details.

Installation

This pipeline itself needs no installation - NextFlow will automatically fetch it from GitHub rbpisupati/nf-haplocaller The pipeline runs with singularity container (based on environment.yml file included with package).

git clone https://github.com/Gregor-Mendel-Institute/nf-haplocaller.git

Running the pipeline

Basic usage

Assuming you have:

  • cloned the repo into directory library
  • a set of BAM files to process in directory data
  • a FASTA file gibing a reference genome to align to in directory data
  • a valid, active installation of NextFlow

then a minimal command to run the pipeline is:

nextflow run library/nf-haplocaller/main.nf \
    --reads "data/*bam" \
    --fasta "data/TAIR10_wholeGenome.fasta" \
    --outdir output_folder \
    -profile cbe

This will take a long time, so it is recommended to run this in a detatchable window, such as tmux.

Options

Here is a full list of arguments and options

Input

  • --reads: Path to input files. This will usually include a wildcard to include all files matching a pattern, and be enclosed in double quotes ("").
  • --fasta: Optional path to a reference fasta file to align reads to.
  • --file_ext: File type of the input files. Options are "bam", 'fastq' and 'aligned_bam'.
  • --singleEnd: Flag for whether data are single- or paired end. Defaults to false.
  • -profile: Give a nextflow profile to allow the pipeline to talk to the job scheduling system on your machine. Valid inputs are:
    • mendel for PBS systems
    • cbe for SLURM systems
    • singularity
    • local to run on a local machine

Processing

  • --saveTrimmed: If true, keep trimmed data. Defaults to false.
  • --notrim: If true, skip trimming reads. Defaults to false
  • --clip_r1 Integer number of bases to trim from the 5` end of read 1.
  • --clip_r2 Integer number of bases to trim from the 5` end of read 2.
  • --three_prime_clip_r1 Integer number of bases to trim from the 3` end of read 1.
  • --three_prime_clip_r2 Integer number of bases to trim from the 3` end of read 2.

Output

  • --project: Project name
  • --outdir: Path to directory for the results.
  • --cohort: Optional. Specify a group of samples to lump together into a single output file.
  • --email: Optional email address to contact when the pipeline finishes.
  • --plaintext_email = If true, send the notification email in plain text.
  • -w: Path to working directory. Defaults to the current working directory. Note that w is preceded by only one hyphen.

Credits

Citation

Please cite the paper below if you use this pipeline.

Pisupati, R. et al.. Verification of Arabidopsis stock collections using SNPmatch, a tool for genotyping high-plexed samples. Nature Scientific Data 4, 170184 (2017). doi:10.1038/sdata.2017.184