Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information

BMC Genomics

Soojin V Yi²  Taesung Park¹  Xingyu Yang²  Iksoo Huh¹ 

[1] Department of Statistics, Bioinformatics and Biostatistics Laboratory, Seoul National University, 151-742 Seoul, Korea;School of Biology, Institute of Bioengineering and Biosciences, Georgia Institute of Technology, 310 Ferst Drive, 30332 Atlanta, GA, USA

关键词: MethylC-seq;    Local DNA methylation level;    Bayes classifier;    DNA methylation;   

Others  :  1216445
DOI  :  10.1186/1471-2164-15-608

 received in 2014-03-15, accepted in 2014-07-07,  发布年份 2014

【 摘 要 】

Background

Whole genome sequencing of bisulfite converted DNA (‘methylC-seq’) method provides comprehensive information of DNA methylation. An important application of these whole genome methylation maps is classifying each position as a methylated versus non-methylated nucleotide. A widely used current method for this purpose, the so-called binomial method, is intuitive and straightforward, but lacks power when the sequence coverage and the genome-wide methylation level are low. These problems present a particular challenge when analyzing sparsely methylated genomes, such as those of many invertebrates and plants.

Results

We demonstrate that the number of sequence reads per position from methylC-seq data displays a large variance and can be modeled as a shifted negative binomial distribution. We also show that DNA methylation levels of adjacent CpG sites are correlated, and this similarity in local DNA methylation levels extends several kilobases. Taking these observations into account, we propose a new method based on Bayesian classification to infer DNA methylation status while considering the neighborhood DNA methylation levels of a specific site. We show that our approach has higher sensitivity and better classification performance than the binomial method via multiple analyses, including computational simulations, Area Under Curve (AUC) analyses, and improved consistencies across biological replicates. This method is especially advantageous in the analyses of sparsely methylated genomes with low coverage.

Conclusions

Our method improves the existing binomial method for binary methylation calls by utilizing a posterior odds framework and incorporating local methylation information. This method should be widely applicable to the analyses of methylC-seq data from diverse sparsely methylated genomes. Bis-Class and example data are provided at a dedicated website (http://bibs.snu.ac.kr/software/Bisclass webcite).

【 授权许可】

   
2014 Huh et al.; licensee BioMed Central Ltd.

【 图 表 】

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