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MetMap Enables Genome-Scale Methyltyping for Determining Methylation States in Populations

Singer, Meromit and Boffelli, Dario and Dhahbi, Joseph and Schönhuth, Alexander and Schroth, Gary P. and Martin, David I. K. and Pachter, Lior (2010) MetMap Enables Genome-Scale Methyltyping for Determining Methylation States in Populations. PLOS Computational Biology, 6 (8). Art. No. e1000888. ISSN 1553-7358. PMCID PMC2924245. https://resolver.caltech.edu/CaltechAUTHORS:20170306-121310345

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[img] PDF (Figure S1. Validation of MetMap predictions by site-specific bisulfite sequencing) - Supplemental Material
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[img] PDF (Table S1. Read counts of the different samples) - Supplemental Material
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[img] PDF (Text S1. Supporting material on MetMap's algorithms and parameters) - Supplemental Material
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[img] PDF (Text S2. Supporting information on MetMap's performance and sensitivity) - Supplemental Material
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Abstract

The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap's inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1371/journal.pcbi.1000888DOIArticle
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000888PublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2924245/PubMed CentralArticle
Additional Information:© 2010 Singer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: April 1, 2010; Accepted: July 15, 2010; Published: August 19, 2010. This work was supported by the NIH grants HL084474 (DB), ES016581 (DM), CA115768 (DM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Meromit Singer and Lior Pachter received no funding for this work. We thank Lu Zhang from Illumina, Inc., Hayward, CA for the MethylSeq data used in this manuscript, the ENCODE project for generation of the FAIRE datasets, Sriram Sankararaman for many enlightening discussions and careful feedback on the manuscript, and Cole Trapnell for critical reading of the manuscript. Author Contributions: Conceived and designed the experiments: MS DB DIKM LP. Performed the experiments: MS JD AS GPS DIKM. Analyzed the data: MS DB JD AS DIKM LP. Contributed reagents/materials/analysis tools: GPS. Wrote the paper: MS DB DIKM LP. The authors have declared that no competing interests exist.
Funders:
Funding AgencyGrant Number
NIHHL084474
NIHES016581
NIHCA115768
Issue or Number:8
PubMed Central ID:PMC2924245
Record Number:CaltechAUTHORS:20170306-121310345
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170306-121310345
Official Citation:Singer M, Boffelli D, Dhahbi J, Schönhuth A, Schroth GP, Martin DIK, et al. (2010) MetMap Enables Genome-Scale Methyltyping for Determining Methylation States in Populations. PLoS Comput Biol 6(8): e1000888. doi:10.1371/journal.pcbi.1000888
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74788
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Mar 2017 21:02
Last Modified:03 Oct 2019 16:43

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