nf-core pipeline ·
nf-core/methylarray
An end-to-end Nextflow pipeline for processing, filtering, and analysing Illumina DNA methylation array data.
What nf-core/methylarray is
nf-core/methylarray is a bioinformatics workflow designed to process and analyse DNA methylation data generated from Illumina arrays. Originally written by Adrian Janucik based on scripts created by Ghada Nouairia, it provides a standardised protocol to take raw array readings through quality control, filtering, and downstream statistical testing.
Illumina methylation arrays present numerous technical challenges, including probe cross-reactivity, confounding genetic variants, sex-chromosome-linked effects, and tissue cell-type heterogeneity. This pipeline automates the handling of these factors, ensuring rigorous quality checks and probe filtering prior to identifying differentially methylated sites.
The pipeline enables researchers to detect differentially methylated positions (DMPs), regions (DMRs), and blocks between experimental groups, whilst optionally estimating and adjusting for cell-type composition in mixed-tissue samples.
What the pipeline does
The workflow begins by using minfi to ingest raw IDAT files, execute quality control checks, normalize signal intensities via preprocessQuantile or preprocessFunnorm, and calculate methylation levels.
Next, the pipeline filters out technical and biological confounders: DNAmCrosshyb removes ambiguously mapping cross-reactive probes; minfi identifies and removes probes overlapping known single-nucleotide polymorphisms (SNPs); and EPIC array annotations are used to exclude X and Y chromosome probes to avoid sex-related bias. Confounding probes linked to external variables such as age are filtered via minfi's dmpFinder.
For multi-cellular samples, particularly whole blood, the pipeline can estimate cell proportions and adjust data using ChAMP's champ.refbase function. Finally, differential methylation analysis is carried out using methods from ChAMP and minfi to detect significantly altered probes, regions, or blocks across sample groups.
Key facts
- Pipeline framework
- Nextflow
- Input format
- Illumina IDAT files
- Supported array types
- Illumina EPIC arrays
- Original developers
- Adrian Janucik and Ghada Nouairia
- Normalization methods
- preprocessQuantile and preprocessFunnorm
- Cell composition adjustment tool
- ChAMP (champ.refbase)
- Probe filtering tools
- minfi and DNAmCrosshyb
What you can use it for
- Epigenome-wide association studies (EWAS)
- Identify differentially methylated positions, regions, or blocks across sample cohorts while controlling for known confounders like age.
- Whole blood methylation analysis
- Estimate and correct for heterogeneous white blood cell type compositions in blood-derived DNA samples using reference-based adjustment.
- Illumina array quality control and probe cleaning
- Filter raw IDAT datasets to eliminate cross-reactive probes, SNP-overlapping loci, and sex-chromosome-associated probes.
Run it here
The research agent runs nf-core/methylarray with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Covers the complete analysis workflow from raw IDAT files to differential methylation testing.
- Includes dedicated filtering steps to eliminate cross-reactive, SNP-associated, and sex chromosome probes.
- Offers automated cell composition estimation and adjustment for whole blood datasets.
- Supports multiple standard normalization algorithms, including quantile and functional normalization.
Watch out for
- Sex chromosome probe filtering is specifically based on EPIC array annotations.
- Cell composition correction is primarily designed for whole blood reference samples.
- Exclusively processes Illumina array data rather than sequencing-based methylation measurements.
Questions
Can I run nf-core/methylarray without my own cluster?
The research agent runs nf-core/methylarray with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/methylarray under?
MIT, like every nf-core pipeline.
What raw file formats does nf-core/methylarray require as input?
The pipeline takes raw Illumina array IDAT files as input, which are read and processed using the minfi package.
Which normalization methods can be used?
The pipeline supports data normalization through either preprocessQuantile or preprocessFunnorm within minfi.
How are cell-type proportion variations addressed?
Cell composition variations are estimated and adjusted using the champ.refbase function from ChAMP, which is particularly tailored for whole blood samples.
How does the pipeline handle confounding variables like age?
It performs differential methylation analysis using minfi's dmpFinder function to detect and filter out probes associated with confounding factors.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/methylarray's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.