PrEMeR-CG: inferring nucleotide level DNA methylation values from MethylCap-seq data.

Frankhouser DE, Murphy M, Blachly JS, Park J, Zoller MW, Ganbat JO, Curfman J, Byrd JC, Lin S, Marcucci G, Yan P, Bundschuh R
Bioinformatics 30 3567-74 12/15/2014

Abstract

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.

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