A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.

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...

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Autores principales:	Qing Xie, Qi Liu, Fengbiao Mao, Wanshi Cai, Honghu Wu, Mingcong You, Zhen Wang, Bingyu Chen, Zhong Sheng Sun, Jinyu Wu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/17f21706f5e54475b523a39cb8f4cb9c
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spelling	oai:doaj.org-article:17f21706f5e54475b523a39cb8f4cb9c2021-11-25T05:40:43ZA Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.1553-734X1553-735810.1371/journal.pcbi.1003853https://doaj.org/article/17f21706f5e54475b523a39cb8f4cb9c2014-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1003853https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358High-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 (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.Qing XieQi LiuFengbiao MaoWanshi CaiHonghu WuMingcong YouZhen WangBingyu ChenZhong Sheng SunJinyu WuPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 9, p e1003853 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Qing Xie Qi Liu Fengbiao Mao Wanshi Cai Honghu Wu Mingcong You Zhen Wang Bingyu Chen Zhong Sheng Sun Jinyu Wu A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
description	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 (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.
format	article
author	Qing Xie Qi Liu Fengbiao Mao Wanshi Cai Honghu Wu Mingcong You Zhen Wang Bingyu Chen Zhong Sheng Sun Jinyu Wu
author_facet	Qing Xie Qi Liu Fengbiao Mao Wanshi Cai Honghu Wu Mingcong You Zhen Wang Bingyu Chen Zhong Sheng Sun Jinyu Wu
author_sort	Qing Xie
title	A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
title_short	A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
title_full	A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
title_fullStr	A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
title_full_unstemmed	A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
title_sort	bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/17f21706f5e54475b523a39cb8f4cb9c
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A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.

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