Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions. Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Sun, Shuying; Yu, Xiaoqing: HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test (2016)
- Yu, Xiaoqing; Sun, Shuying: HMM-DM: identifying differentially methylated regions using a hidden Markov model (2016)
- Yu, Xiaoqing; Sun, Shuying: Comparing five statistical methods of differential methylation identification using bisulfite sequencing data (2016)