ArticleBMC genomics2015
Detection of differentially methylated regions from bisulfite-seq data by hidden Markov models incorporating genome-wide methylation level distributions.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 66 citations in OpenAlex.
- Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Evaluation of Illumina and Oxford Nanopore Sequencing for the Study of DNA Methylation in Alzheimer's Disease and Frontotemporal Dementia.International journal of molecular sciences · 2025Review
- Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model.Biomolecules · 2024Article
- Existence and possible roles of independent non-CpG methylation in the mammalian brain.DNA research : an international journal for rapid publication of reports on genes and genomes · 2020Article
- Preliminary Analysis of Within-Sample Co-methylation Patterns in Normal and Cancerous Breast Samples.Cancer informatics · 2019Article
- A survey of the approaches for identifying differential methylation using bisulfite sequencing data.Briefings in bioinformatics · 2018Review
- GIW and InCoB, two premier bioinformatics conferences in Asia with a combined 40 years of history.BMC genomics · 2015Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetection 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.
resultsWe 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.
conclusionsOur 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 http://epigenome.cbrc.jp/bisulfighter.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.