methylR 2.0: an enhanced user-friendly solution for DNA methylation array analysis
A Single Shiny Solution from Sequencer to Pathway Analysis
A brief overview of DNA Methylation
- The human genome contains ~32 million CpGs. VERY different between different species
- CpGs are not evenly spaced across the genome. Tend to be present in clusters, CpG islands
- CpG methylation can regulate gene expression.
DNA Methylation - Why its important?
What are methylation arrays and how do they work?
Methylation arrays are based on SNP array technology
- The Infinium Methylation Assay uses two different bead types to detect CpG methylation.
- The U bead type matches the unmethylated CpG site;
- the M bead type matches the methylated site.
In the top figure, the unmethylated CpG target site matches with the U probe, enabling single-base extension and detection. It has a single-base mismatch to the M probe, which inhibits extension. If the CpG locus of interest is methylated (bottom figure), the reverse occurs.
Measurement of Methylation Status
- In the Illumina DNA Methylation Array, we measure methylation in a population of cells.
- Individual cell can be either 0, 0.5 or 1 at one CpG.
- Across a population, we get a continuous measurement between [0 - 1].
Measurement of Methylation Status
Illumina arrays measure both methylated (C) and unmethylated (T) signal to get proportion of methylation at a CpG.
\[\beta = \frac{Meth}{Meth + Unmeth}\]
Intuitive, easy to interpret, great for visualization
\[M = log_2 \frac{Meth}{Unmeth}\]
Better statistical properties, recommended for statistical testing
\[ M = log_2 \frac{\beta}{1 - \beta}\]
Can convert between them via a logit transformation.
Array Data Analysis Pipeline/Tools
Remove bad samples and poor performing probes (CpGs)
Transform data to remove unwanted variation
Estimate means and variancs and borrow information acorss probes
Biological interpretation of results
e.g., gene expression
minfi, ChAMP, methylumi, limma, RnBeads, …
minfi, ChAMP, methylumi, limma, RnBeads, waterMelon, …
limma, bumhunter, DMRcate, …
Gviz, IGV, …
GenomicRanges
Why MethylR
- Zero coding
- No installation (webserver)
- Graphical User Interface with error messages
- Free, open access secure webserver
- Multi-OS/multi-browser support
- Well-known open-source packages
<a href="https://sourceforge.net/projects/methylr/" target="_blank">methylr</a>MethylR: Publication and Stats
How it works - webserver
What happens after you upload your data –
- methylR will start analyzing your data as you selected,
- Your data and result will be lost once you close the browser
- No storage for your data or analysis result.
- Only you from your opened browser can see your result.
- Once you open a new browser window, it will open a new session with a new container.
Please remember:
- Univerity IT department restart the server without any prior notice.
- We CANNOT recover your data or result.
How it works - Local server
For upto 100 samples (850K)
docker run --rm -p 3838:3838 \
jd21/methylr:latestFor upto 100 samples (935K)
docker run --rm -p 3838:3838 \
jd21/<upcoming>methylR - Workflow
Component tools
Input Data Processing
methylR vs methylR 2.0
methylR Component tools
methylR 2.0 Component tools
methylR 2.0- An Overview
- Session information
- QC result
- Normalized data table
- DMC table
- DMR table
- Cell type heterogeneity plot
- Download results
[1] "R version 4.3.2 (2023-10-31)"
[2] "Platform: x86_64-pc-linux-gnu (64-bit)"
[3] "Running under: Ubuntu 22.04.4 LTS"
[4] ""
[5] "Matrix products: default"
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[395] "[238] fontquiver_0.2.1 "
[396] "[239] miniUI_0.1.1.1 "
[397] "[240] gtable_0.3.4 "
[398] "[241] abind_1.4-5 "
[399] "[242] xfun_0.43 "
[400] "[243] curl_5.2.1 "
[401] "[244] GEOquery_2.70.0 "
[402] "[245] KernSmooth_2.23-22 "
[403] "[246] styler_1.10.3 "
[404] "[247] survival_3.5-7 "
[405] "[248] goseq_1.54.0 "
[406] "[249] jsonlite_1.8.8 "
[407] "[250] shinyjqui_0.4.1 "
[408] "[251] svglite_2.1.3 "
[409] "[252] base64enc_0.1-3 "
[410] "[253] scrime_1.3.5 "
[411] "[254] billboarder_0.4.1 "
[412] "[255] Matrix_1.6-1.1 "
[413] "[256] Gviz_1.46.1 "
[414] "[257] shinytoastr_2.2.0 "
[415] "[258] fastmatch_1.1-4 "
[416] "[259] gower_1.0.1 "
[417] "[260] checkmate_2.3.1 "
[418] "[261] hardhat_1.3.1 "
[419] "[262] gtools_3.9.5 "
[420] "[263] htmlTable_2.4.2 "
[421] "[264] bit64_4.0.5 "
[422] "[265] farver_2.1.1 " 
Compare results with the published dataset
- We used GSE207426 dataset to test our methylR pipeline for Illumina HumanMethylation EPIC array (v1).
- We found the same number of DMCs in the full dataset (123 510 DMCs) as well as in the TSS (both TSS200 and TSS1500) + 5′ UTRs subset (26 626 DMCs).
- We obtained same number (37) of enriched pathways using REACTOME database.
The Development Team
Massimiliano Volpe
Department of Immunotechnology, Medicon Village, Lund University, Sweden
Manual Preapration, β-testing, UI Designing, Manuscript Writing.
Marco Miralto
Department of Research Infrastructures for marine biological resources, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Italy
Docker Building, Manuscript Preparation,
Michele Longo
Department of Translational Medicine, Federico II University of Naples, Naples, Italy
Testing, Manuscript Preparation,
Jyotirmoy Das
Bioinformatics, Core Facility, Cell Biology, Faculty of Medical and Health Sciences, Linköping University, Sweden
Coding, Debugging, Development, UI Design, Testing, Manuscript writing.
