Introduction

icgc-argo-workflows/dnaalnqc is a reproducible bioinformatics workflow that can be used to obtain QC metrics from tumour/normal paired, tumour-only, normal-only WGS/WXS/Targeted-Seq aligned reads. It has been created to support quality control efforts within ICGC-ARGO project. The aggregated QC metrics are formed to align with the GA4GH WGS_Quality_Control_Standards.

The workflow 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. The workflow has adopted nf-core framework and best practice guidelines to ensure reproducibility, portability and scalability. Where possible, many processes have been installed from nf-core/modules. Moreover, ICGC ARGO specific modules have been installed form icgc-argo-workflows/argo-modules, which hosts ARGO reusable modules across all ICGC ARGO pipelines!

Requirements

1. Install Nextflow (>=22.10.1)

2. Install Docker.

3. Stage the required reference files

Quick start

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

   nextflow run icgc-argo-workflows/dnaalnqc \
     -profile test,standard \
     --outdir <OUTDIR>

2. 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/dnaalnqc \
     -profile test_rdpc_qa,standard \
     --api_token <YOUR_API_TOKEN> \
     --reference_base <REFERENCE_BASE> \
     --outdir <OUTDIR>

Usage

Workflow 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
• Perform Samtools Stats to collect metrics related to sequencing library quality and read characteristics
• Perform Picard CollectOxoGMetrics to collect OxoG metrics
• Perform Picard CollectQualityYieldMetrics to collect read characteristics metrics
• Perform Picard CollectWGSMetrics to collect coverage metrics for WGS
• Perform Picard CollectHSMetrics to collect coverage metrics for WXS and Targeted-Seq
• Perfomr GATK CalculateContamination to collect cross sample contamination rate
• Perform MultiQC analysis to generate aggregated results
• (RDPC mode only) Generate SONG metadata for all collected QC metrics files and upload QC files to SONG/SCORE

References

• Reference genome:

  • GRCh38 reference genome fasta file. The file can be downloaded by:

    wget https://swengbioinfo.blob.core.windows.net/genomics-public-data/reference-genome/GRCh38_hla_decoy_ebv/GRCh38_hla_decoy_ebv.fa

  • GRCh38 reference genome fasta index file. The file can be downloaded by:

    wget https://swengbioinfo.blob.core.windows.net/genomics-public-data/reference-genome/GRCh38_hla_decoy_ebv/GRCh38_hla_decoy_ebv.fa.fai

  • GRCh38 reference genome sequence dictionary file. The file can be downloaded by:

    wget https://swengbioinfo.blob.core.windows.net/genomics-public-data/reference-genome/GRCh38_hla_decoy_ebv/GRCh38_hla_decoy_ebv.dict

• GATK resources:
  • germline_resource and index files. The files can be downloaded by:

    wget https://swengbioinfo.blob.core.windows.net/genomics-public-data/gatk-resources/af-only-gnomad.pass-only.biallelic.snp.hg38.vcf.gz
    wget https://swengbioinfo.blob.core.windows.net/genomics-public-data/gatk-resources/af-only-gnomad.pass-only.biallelic.snp.hg38.vcf.gz.tbi

• Autosome non-gap regions
  • autosome_non_gap bed file was downloaded from NPM-sample-qc and staged under project folder assets

NOTE Please stage the reference files into the reference directory <REFERENCE_BASE> with the following folder structure

<REFERENCE_BASE>
├── GRCh38_hla_decoy_ebv.dict
├── GRCh38_hla_decoy_ebv.fa
├── GRCh38_hla_decoy_ebv.fa.fai
├── gatk_resource
│   ├── af-only-gnomad.pass-only.biallelic.snp.hg38.vcf.gz
│   └── af-only-gnomad.pass-only.biallelic.snp.hg38.vcf.gz.tbi

Inputs

Local mode

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

sample_sheet.csv:

sample,bam_cram,bai_crai(optional),patient(optional),status(optional),sex(optional)
CONTROL_REP1_SAMPLE0,CONTROL_REP_0.bam,CONTROL_REP_0.bam.bai,CONTROL_REP1_DONOR,0,XX
CONTROL_REP1_SAMPLE1,CONTROL_REP_1.bam,CONTROL_REP_1.bam,bai,CONTROL_REP1_DONOR,1,XX

Each row represents an aligned BAM or CRAM from a sample.

Then, you need to download all required reference files, and stage them into a directory <REFERENCE_BASE>

Now, you can run the workflow using:

nextflow run icgc-argo-workflows/dnaalnqc \
   -profile resource,<standard/singularity> \
   --local_mode true \
   --input sample_sheet.csv \
   --reference_base <REFERENCE_BASE> \
   --outdir <OUTDIR>

RDPC mode

You can run the workflow in RDPC mode by using:

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

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.