seqqc: quality control of raw sequencing data

Introduction

Arcadia-Science/seqqc is an analysis pipeline for quality control for sequencing data. The pipeline can be used on short or long read sequencing data (in FASTQ format) to identify common problems like the presence of adapters, high sequencing duplication rates, mislabelled samples, and contamination.

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 full-sized data sets 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. See the seqqc folder in the Arcadia Science test data set repository for more information about the full test data sets.

Pipeline summary

1. Raw read QC (FastQC)
2. Contamination detection (sourmash)
3. Sample sequence similarity measurement (sourmash)
4. Present pipeline results (MultiQC)

Quick Start

1. Install Nextflow (>=22.10.1)

2. Install either Docker or Singularity (you can follow this tutorial) 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). Note that in the current release, the sourmash results will only be integrated into the MultiQC report if the pipeline is executed with the Docker profile. We'll update the pipeline as soon as the sourmash MultiQC modules are merged and released as part of MultiQC.

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

   nextflow run Arcadia-Science/seqqc -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 Arcadia-Science/seqqc --input samplesheet.csv --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Credits

Arcadia-Science/seqqc was originally written by scientists at Arcadia Science.

Contributions and Support

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

Citations

If you use Arcadia-Science/seqqc for your analysis, please cite it using the following DOI: 10.57844/arcadia-cxn6-ch62

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.