README.md

CenterForMedicalGeneticsGhent/nf-cmgg-preprocessing

Introduction

CenterForMedicalGeneticsGhent/nf-cmgg-preprocessing is a bioinformatics best-practice analysis pipeline for sequencing data preprocessing at CMGG. This workflow handles demultiplexing, alignment, qc and archiving.

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!

Pipeline summary

1. Demultiplexing
2. Fastq preprocessing
3. Alignment
4. QC
5. Coverage analysis
6. Compression

Quick Start

1. Install Nextflow (>=22.10.0)

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 CenterForMedicalGeneticsGhent/nf-cmgg-preprocessing -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 CenterForMedicalGeneticsGhent/nf-cmgg-preprocessing --input flowcells.csv --samples samples.csv --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Flowchart

flowchart TB

FC(["Flowcell (BCL)"])                          --> DEMULTIPLEX
SS([SampleSheet])                               --> DEMULTIPLEX

subgraph DEMULTIPLEX[Demultiplex]
    direction LR
    SAMPLESHEET([SampleSheet])                  --> BCLCONVERT[bcl-convert]
    FLOWCELL([Flowcell])                        --Split by LANE--> BCLCONVERT[bcl-convert]
    BCLCONVERT                                  --> DEMUX_FASTQ([Fastq])
    BCLCONVERT                                  --> DEMULTIPLEX_STATS([Demultiplex Reports])
end

DEMULTIPLEX                                     --> FASTP[FastP: Trimming and QC]
DEMULTIPLEX                                     --> DEMUX_REPORTS
FASTP                                           --> IS_HUMAN{Human data?}
IS_HUMAN                                        --YES--> ALIGNMENT
IS_HUMAN                                        --NO--> FASTQTOSAM
FASTQTOSAM[Picard FastqToSam]                   --> UNALIGNED_BAM([Unaligned BAM]) --> A_CRAM

subgraph ALIGNMENT
    direction TB

    subgraph ALIGNER
        direction LR
        BOWTIE2[bowtie2-align] & SNAP[snap-aligner] --> SORT[Sorting]
    end

    ALIGNER --> BamSorMaDUP
    BamSorMaDUP                                 --> SORTBAM[Sorted/markdup bam] & MARKDUP_METRICS([Markduplicates Metrics])
    SORTBAM                                     -->  ALN_CRAM([CRAM]) & MOSDEPTH[Mosdepth] & BAMQC[BAM QC Tools]
    MOSDEPTH                                    -->  COVERAGE_BED([Coverage BEDs]) & COVERAGE_METRICS([Coverage Metrics])
    BAMQC & MARKDUP_METRICS & COVERAGE_METRICS  -->  ALN_MULTIQC[MultiQC]
end

ALIGNMENT                                       --> A_CRAM([CRAM])
ALIGNMENT                                       --> A_BAM_METRICS([BAM metrics])
ALIGNMENT                                       --> A_COVERAGE_METRICS([Coverage metrics])
ALIGNMENT                                       --> A_COVERAGE_BED([Coverage BED])

A_BAM_METRICS                                   --> MQC([Run MultiQC Report])
A_COVERAGE_METRICS                              --> MQC
FASTP                                           --> MQC
DEMUX_REPORTS                                   --> MQC

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Credits

CenterForMedicalGeneticsGhent/nf-cmgg-preprocessing was originally written by Matthias De Smet.

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.