A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data

Qing Xie, Qi Liu, Fengbiao Mao, Wanshi Cai, Honghu Wu, Mingcong You, Zhen Wang, Bingyu Chen, Zhong Sheng Sun and Jinyu Wu

PLOS Computational Biology, 2014, vol. 10, issue 9, 1-8

Abstract: High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003853

DOI: 10.1371/journal.pcbi.1003853

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