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
The pipeline allows you to choose between running either Bismark or bwa-meth / MethylDackel.
Choose between workflows by using --aligner bismark (default, uses bowtie2 for alignment), --aligner bismark_hisat or --aligner bwameth. For higher performance, the pipeline can leverage the Parabricks implementation of bwa-meth (fq2bammeth), which implements the baseline tool bwa-meth in a performant method using fq2bam (BWA-MEM + GATK) as a backend for processing on GPU. To use this option, include the gpu profile along with --aligner bwameth.
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 |
|---|---|---|
| Generate Reference Genome Index (optional) | Bismark | bwa-meth |
| Merge re-sequenced FastQ files | cat | cat |
| Raw data QC | FastQC | FastQC |
| Adapter sequence trimming | Trim Galore! | Trim Galore! |
| Align Reads | Bismark (bowtie2/hisat2) | bwa-meth |
| Deduplicate Alignments | Bismark | Picard MarkDuplicates |
| Extract methylation calls | Bismark | MethylDackel |
| Sample report | Bismark | - |
| Summary Report | Bismark | - |
| Alignment QC | Qualimap (optional) | Qualimap (optional) |
| Sample complexity | Preseq (optional) | Preseq (optional) |
| Project Report | MultiQC | MultiQC |
Optional targeted sequencing analysis is available via --run_targeted_sequencing and --target_regions_file; see the usage documentation for details.
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.
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.
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 (@Hammarn)
- Alexander Peltzer (@apeltzer)
- Patrick Hüther (@phue)
- Maxime U Garcia (@maxulysse)
If you would like to contribute to this pipeline, please see the contributing guidelines.
For further information or help, don't hesitate to get in touch on the Slack #methylseq channel (you can join with this invite).
If you use nf-core/methylseq for your analysis, please cite it using the following doi: 10.5281/zenodo.1343417
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.
You can cite the nf-core publication as follows:
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.