Analyzing methylation array data requires robust tools to ensure accurate and meaningful insights. Over the years, various analysis tools have been developed for Illumina Infinium Methylation BeadChips, ranging from Illumina-hosted software to third-party Bioconductor packages. These tools have been instrumental in advancing methylation array applications and epigenetic discoveries.
For BeadChip processing laboratories
The cloud-based DRAGEN Array Methylation QC software delivers high-throughput and quantitative reporting of control metrics for Infinium Methylation microarrays. Read more about the sample QC methods used to determine data quality.
GenomeStudio Methylation Module and BeadArray Controls Reporter
The GenomeStudio Methylation Module can be used for basic QC of methylation beadchips. The Controls Dashboard in GenomeStudio is used to visualize sample-independent and sample-dependent controls, whereas the BeadArray Controls reporter (BACR) provides a quantitative analysis of controls for fast results.
Partek Flow by Illumina offers interactive visualization, powerful statistics, and comprehensive analysis of methylation array data. The user-friendly interface empowers researchers of all skill levels to analyze their data with confidence.
| Illumina Software for Methylation Array QC | Illumina Software for Downstream Analysis | |||
| DRAGEN Array Methylation QC |
BeadArray Controls Reporter | GenomeStudio Methylation Module |
Partek Flow | |
|---|---|---|---|---|
| Key uses | Approachable analysis optimized for control calling | Quantitative quality check | Visual quality check | Multiomics discovery |
| Deployment | Cloud– Graphical user interface |
Local – Graphical User Interface | Local – Graphical User Interface | Cloud–Graphical user interface Customer-hosted – Graphical User Interface |
| QC capabilities | 21 quantitative control metrics Data summary and PCA plots Proportion of passing assays (p-value pass) |
21 quantitative control metrics | Control probe-based control plots Hierarchical clustering | Proportion p-value pass PCA control plots SVD |
| Analysis capabilities | Detection p-values Beta-values M-values |
None | Detection p-values Beta-values (not recommended) Differential methylation (not recommended) |
Detection p-values Beta-values M-values Differential methylation |
| Price | Included with array, nominal compute and storage iCredit charges apply. Requires access to BSSH via an ICA Basic* subscription. |
No charge, included with array. Download from support site |
No charge, included with array. Download from support site |
Charges vary by license type (Lab or Enterprise), number of year commitment, and number of seats needed. |
* Availability of recommended thresholds and control probes varies
SeSAMe provides end-to-end data analysis of Infinium Methylation BeadChips including advanced QC, updated normalization techniques, differential methylation analysis, and visualization capabilities.
The following video tutorial series, led by SeSAMe developer Wanding Zhou, provides step-by-step tutorials to familiarize new users with data analysis on SeSAMe:
In this video you’ll learn how to install SeSAMe to perform data analysis for the Infinium DNA methylation beadchip. All the scripts and links can be found on this SeSAMe Installation Github page. If you haven't installed R on your computer yet, please do so before watching this video.
This video tutorial will show you how to process IDATs into DNA methylation level data, or the beta values. This tutorial uses two public datasets from Gene Expression Omnibus or GEO. You’ll learn how to read in the signal intensity data, perform quality control, assess results, and more.
In this video, we’ll go over some of the linear modeling-based frameworks for analyzing differential DNA methylation. You’ll learn how to load packages and data, what to consider and check for prior to modeling, perform linear modeling, and investigate biological questions following test results.
This video tutorial will demonstrate how to use the SeSAMe software to infer sample metadata. This metadata can be sex, age, DNA copy number or cell fraction, or other metadata. This tutorial demonstrates various inferences to provide a broad understanding of the process.
Additional information and full documentation can be found on the SeSAMe Bioconductor page.
Minfi is a comprehensive package for methylation data analysis developed by Kasper Hansen. Github packages may be available to support the use of minfi with newer Infinium Methylation BeadChips. Visit the minfi Bioconductor page for documentation including user guides and installation instructions. Archived tutorial videos using 450K data can be found here, and an introduction video by Kasper Hansen can be found here.
Bioconductor hosts a suite of publicly available software programs to analyze Infinium methylation array data.
The table below provides a few examples of analysis packages and their function:
| Software package | Function |
|---|---|
| ChAMP | Comprehensive R package for Epigenome-Wide Association Study (EWAS), providing pre-processing, differential calling, GSEA and interactive visualization |
| Rnbeads | End-to-end methylation array analysis: includes quality control, data preprocessing, data tracks & tables, exploratory analysis, and differential methylation |
| Conumee | Performs copy number variation (CNV) analysis using Illumina 450K or EPIC Methylation Arrays |
| wateRmelon | Provides a set of tools for importing, quality control, and normalizing Illumina DNA methylation array data |
| bumphunter | Detects differentially methylated regions in EWAS based on ‘bump hunting’ statistical method |
For additional information and methylation array data processing packages, visit Bioconductor.
The Columbia Epigenetics Boot Camp offers an intensive hands-on training on data analysis techniques for methylation arrays, and provides an overview of considerations when designing DNA methylation studies.