Introduction

sage/dcqc is a bioinformatics best-practice analysis pipeline for Nextflow Workflow for Data Coordination Quality Control.

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, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources.

Pipeline summary

1. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)

Pipeline Flow

  flowchart LR;
    subgraph PREPARE TESTS;
    A[CREATE TARGETS]-->B[CREATE TESTS];
    end;
    subgraph INTERNAL TESTS;
    B-->C[COMPUTE TEST];
    end;
    subgraph EXTERNAL TESTS;
    B-->D[CREATE PROCESS];
    D-->E[RUN PROCESS];
    E-->F[COMPUTE TEST];
    end;
    subgraph PREPARE REPORTS;
    C-->G[CREATE SUITE];
    F-->G;
    G-->H[COMBINE SUITES];
    H-->I[UPDATE INPUT CSV];
    end;

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Quick Start

1. Install Nextflow (>=22.10.4)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run sage/dcqc -profile test,YOURPROFILE --outdir <OUTDIR>

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   nextflow run sage/dcqc --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Special Considerations for Running nf-dcqc on Nextflow Tower

nf-dcqc leverages the reports feature when executed on Tower. This is done by pointing Tower to the generated output.csv file which is saved to params.outdir after a successful run. By default, the outdir for the workflow is set to a local directory called results. This does not work on Nextflow Tower runs, as you will not have access to the results directory once the job has completed. Thus, the outdir should be set to an S3 bucket location that the Tower workspace you are using has access to. For example, in the pipeline parameters for a Tower run, you can provide YAML such as:

outdir: s3://example-project-tower-bucket/dcqc_output

From the reports tab within your workflow run, you can view and download the generated output.csv file.

Credits

sage/dcqc was originally written by Bruno Grande bruno.grande@sagebionetworks.org.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

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.