/prealnqc

Primary LanguagePythonMIT LicenseMIT

Introduction

icgc-argo-workflows/prealnqc is a reproducible bioinformatics best-practice analysis pipeline of ICGC ARGO Pre Alignment QC Workflow for DNA/RNA Sequencing Reads.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, many processes have been installed from nf-core/modules. Specifically, ICGC ARGO specific modules have been submitted and installed form icgc-argo-workflows/argo-modules, in order to make them available to all ICGC ARGO pipelines!

Quick Start

  1. Install Nextflow (>=22.10.1)

  2. Install Docker.

  3. Test the workflow running in Local mode on a minimal dataset with a single command:

    nextflow run icgc-argo-workflows/prealnqc -profile test,standard

  4. Test the workflow running in RDPC mode with a single command if you have access to RDPC-QA env and have your valid api_token available:

    nextflow run icgc-argo-workflows/prealnqc -profile rdpc_qa,test_rdpc_qa,standard --api_token <YOUR_API_TOKEN>

  5. Start running your own analysis!

    If you are getting the input data from & sending output data to ICGC-ARGO data center, and you have valid api_token, you can run the workflow with:

    nextflow run icgc-argo-workflows/prealnqc -profile <rdpc,rdpc_qa,rdpc_dev>,standard --api_token <YOUR_API_TOKEN> --study_id <STUDY_ID> --analysis_ids <ANALYSIS_IDS>

    Otherwise, you can provide the path to the input data in samplesheet.csv and run the workflow with:

    nextflow run icgc-argo-workflows/prealnqc -profile standard --input samplesheet.csv --outdir <OUTDIR>

Pipeline summary

Depending on where the input data are coming from and output data are sending to, the workflow can be running in two modes: Local and RDPC . The major tasks performed in the workflow are:

  • (RDPC mode only) Download input sequencing metadata/data from data center using SONG/SCORE client tools
  • (RDPC mode only) Preprocess input sequencing reads (in FASTQ or BAM) into FASTQ file(s) per read group
  • Perform FastQC analysis for FASTQ file(s) per read group
  • Perform Cutadapt analysis for FASTQ file(s) per read group
  • Perform MultiQC analysis to generate aggregated results
  • (RDPC mode only) Generate SONG metadata for all collected QC metrics files and upload them to SONG/SCORE

Inputs

Local mode

First, prepare a sample sheet with your input data that looks as following:

sample_sheet.csv:

sample,lane,fastq_1,fastq_2,single_end(optional)
TEST,C0HVY.2,C0HVY.2_r1.fq.gz,C0HVY.2_r2.fq.gz,false
TEST,D0RE2.1,D0RE2.1_r1.fq.gz,D0RE2.1_r2.fq.gz
TEST,D0RH0.2,D0RH0.2_r1.fq.gz,D0RH0.2_r2.fq.gz

Each row represents a read_group of sequencing reads from a sample.

Now, you can run the workflow using:

nextflow run icgc-argo-workflows/prealnqc \
   -profile <standard/singularity> \
   --local_mode true \
   --input sample_sheet.csv \
   --outdir <OUTDIR>

RDPC mode

You can run the workflow in RDPC mode by using:

nextflow run icgc-argo-workflows/prealnqc \
  -profile <rdpc,rdpc_qa,rdpc_dev>,<standard/singularity> \
  --local_mode false \
  --study_id <STUDY_ID> \
  --analysis_ids <ANALYSIS_IDS> \
  --api_token <YOUR_API_TOKEN> \ 
  --outdir <OUTDIR>

NOTE Please provide workflow parameters via the CLI or Nextflow -params-file option.

Outputs

Upon completion, you can find the aggregated QC metrics under directory:

/path/to/outdir/prep_metrics/<sample_id>.argo_metrics.json

Credits

icgc-argo-workflows/prealnqc was mostly written by Linda Xiang (@lindaxiang), with contributions from Andrej Benjak, Charlotte Ng, Desiree Schnidrig, Edmund Su, Miguel Vazquez, Morgan Taschuk, Raquel Manzano Garcia, Romina Royo and ICGC-ARGO Quality Control Working Group.

Authors (alphabetical)

  • Andrej Benjak
  • Charlotte Ng
  • Desiree Schnidrig
  • Edmund Su
  • Linda Xiang
  • Miguel Vazquez
  • Morgan Taschuk
  • Raquel Manzano Garcia
  • Romina Royo

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.