title: A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.
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 (<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.
authors.affiliation: Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Xie
authors.affiliation: Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
authors.forename: Fengbiao
authors.initials: F
authors.lastname: Mao
authors.affiliation: Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
authors.forename: Wanshi
authors.initials: W
authors.lastname: Cai
authors.affiliation: Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.
authors.forename: Honghu
authors.initials: H
authors.lastname: Wu
authors.affiliation: Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.
authors.forename: Mingcong
authors.initials: M
authors.lastname: You
authors.affiliation: Institute of Molecular Medicine, Department of blood transfusion, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Institute of Molecular Medicine, Department of blood transfusion, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
authors.forename: Bingyu
authors.initials: B
authors.lastname: Chen
authors.affiliation: Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China; Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
authors.forename: Zhong Sheng
authors.initials: ZS
authors.lastname: Sun
authors.affiliation: Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.
authors.forename: Jinyu
authors.initials: J
authors.lastname: Wu
mesh_terms: D044503:5-Methylcytosine / Q000032:analysis* / Q000737:chemistry; D000465:Algorithms; D001499:Bayes Theorem; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D013447:Sulfites / Q000737:chemistry*
chemical_list: D013447:Sulfites; D044503:5-Methylcytosine; C042345:hydrogen sulfite