Detection of differentially methylated regions from bisulfite-seq data by hidden Markov models incorporating genome-wide methylation level distributions

BMC Genomics. 2015;16 Suppl 12(Suppl 12):S3. doi: 10.1186/1471-2164-16-S12-S3. Epub 2015 Dec 9.


Background: Detection of differential methylation between biological samples is an important task in bisulfite-seq data analysis. Several studies have attempted de novo finding of differentially methylated regions (DMRs) using hidden Markov models (HMMs). However, there is room for improvement in the design of HMMs, especially on emission functions that evaluate the likelihood of differential methylation at each cytosine site.

Results: We describe a new HMM for DMR detection from bisulfite-seq data. Our method utilizes emission functions that combine binomial models for aligned read counts, and beta mixtures for incorporating genome-wide methylation level distributions. We also develop unsupervised learning algorithms to adjust parameters of the beta-binomial models depending on differential methylation types (up, down, and not changed). In experiments on both simulated and real datasets, the new HMM improves DMR detection accuracy compared with HMMs in our previous study. Furthermore, our method achieves better accuracy than other methods using Fisher's exact test and methylation level smoothing.

Conclusions: Our method enables accurate DMR detection from bisulfite-seq data. The implementation of our method is named ComMet, and distributed as a part of Bisulfighter package, which is available at

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Binomial Distribution
  • Computational Biology / methods*
  • Cytosine / metabolism
  • DNA Methylation*
  • Genome, Human
  • Genome-Wide Association Study
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Markov Chains
  • Sequence Analysis, DNA / methods*
  • Sulfites


  • Sulfites
  • Cytosine
  • hydrogen sulfite