Methylseq

<h1> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/images/nf-core-methylseq_logo_dark.png"> <img alt="nf-core/methylseq" src="docs/images/nf-core-methylseq_logo_light.png"> </picture> </h1>

Introduction

nf-core/methylseq is a bioinformatics analysis pipeline used for Methylation (Bisulfite) sequencing data. It pre-processes raw data from FastQ inputs, aligns the reads and performs extensive quality-control on the results.

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 / Podman / Charliecloud / Apptainer containers making installation trivial and results highly reproducible.

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.The results obtained from the full-sized test can be viewed on the nf-core website.

Read more about Bisulfite Sequencing & Three-Base Aligners used in this pipeline here

Pipeline Summary

The pipeline allows you to choose between running either Bismark, bwa-meth / MethylDackel or BWA-Mem plus rastair for TAPS data processing. rastair can also be used with bwa-meth aligned reads by setting the aligner to --aligner bwameth and adding the flag --taps.

Choose between workflows by using --aligner bismark (default, uses bowtie2 for alignment), --aligner bismark_hisat, --aligner bwameth or --aligner bwamem. For higher performance, the pipeline can leverage the Parabricks implementation of bwa-meth (fq2bammeth) and the Parabricks implementation of bwa-mem (fq2bammemh), which implement the baseline tools bwa-meth and bwa-mem. To use this option, include the gpu profile along with --aligner bwameth or --aligner bwamem.

Note: For faster CPU runs with BWA-Meth, enable the BWA-MEM2 algorithm using --use_mem2. The GPU pathway (Parabricks) requires -profile gpu and a container runtime (Docker, Singularity, or Podman); Conda/Mamba are not supported for the GPU module.

| Step | Bismark workflow | bwa-meth workflow | bwa-mem + TAPS workflow | | -------------------------------------------- | ------------------------ | --------------------- | -------------------------- | | Generate Reference Genome Index (optional) | Bismark | bwa-meth | bwa index | | Merge re-sequenced FastQ files | cat | cat | cat | | Raw data QC | FastQC | FastQC | FastQC | | Adapter sequence trimming | Trim Galore! | Trim Galore! | Trim Galore! | | Align Reads | Bismark (bowtie2/hisat2) | bwa-meth | bwa mem | | Deduplicate Alignments | Bismark | Picard MarkDuplicates | Picard MarkDuplicates | | Extract methylation calls | Bismark | MethylDackel | TAPS subworkflow (rastair) | | Sample report | Bismark | - | - | | Summary Report | Bismark | - | - | | Alignment QC | Qualimap (optional) | Qualimap (optional) | Qualimap (optional) | | Sample complexity | Preseq (optional) | Preseq (optional) | Preseq (optional) | | Project Report | MultiQC | MultiQC | MultiQC |

Optional targeted sequencing analysis is available via --run_targeted_sequencing and --target_regions_file; see the usage documentation for details.

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1,fastq_2,genome
SRR389222_sub1,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub1.fastq.gz,,
SRR389222_sub2,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub2.fastq.gz,,
SRR389222_sub3,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub3.fastq.gz,,
Ecoli_10K_methylated,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/Ecoli_10K_methylated_R1.fastq.gz,https://github.com/nf-core/test-datasets/raw/methylseq/testdata/Ecoli_10K_methylated_R2.fastq.gz,

Each row represents a fastq file (single-end) or a pair of fastq files (paired end).

Now, you can run the pipeline using default parameters as:

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

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/methylseq was originally written by Phil Ewels (@ewels), and Sateesh Peri (@sateeshperi) is its active maintainer.

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

• Felix Krueger (@FelixKrueger)
• Edmund Miller (@EMiller88)
• Rickard Hammarén ([@Hammar