General Information
| Description | Inferring nucleotide level DNA methylation values from MethylCap-seq data |
| Homepage | http://bioserv.mps.ohio-state.edu/ |
Publications
PrEMeR-CG: inferring nucleotide level DNA methylation values from MethylCap-seq data
Motivation
: DNA methylation is an epigenetic change occurring in genomic CpG sequences that contribute to the regulation of gene transcription both in normal and malignant cells. Next-generation sequencing has been used to characterize DNA methylation status at the genome scale, but suffers from high sequencing cost in the case of whole-genome bisulfite sequencing, or from reduced resolution (inability to precisely define which of the CpGs are methylated) with capture-based techniques.
Results
: Here we present a computational method that computes nucleotide-resolution methylation values from capture-based data by incorporating fragment length profiles into a model of methylation analysis. We demonstrate that it compares favorably with nucleotide-resolution bisulfite sequencing and has better predictive power with respect to a reference than window-based methods, often used for enrichment data. The described method was used to produce the methylation data used in tandem with gene expression to produce a novel and clinically significant gene signature in acute myeloid leukemia. In addition, we introduce a complementary statistical method that uses this nucleotide-resolution methylation data for detection of differentially methylated features.
Availability
: Software in the form of Python and R scripts is available at http://bioserv.mps.ohio-state.edu/ premer and is free for non-commercial use.
Contact
: pearlly.yan@osumc.edu ; bundschuh@mps.ohio-state.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
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pETM
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A penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data
DM-BLD
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Differential methylation detection using a hierarchical Bayesian model exploiting local dependency
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