Economics at your fingertips  

HMM-DM: identifying differentially methylated regions using a hidden Markov model

Yu Xiaoqing () and Sun Shuying
Additional contact information
Yu Xiaoqing: Department of Biostatistics, Yale University, New Haven, CT 06511, USA
Sun Shuying: Department of Mathematics, Texas State University, San Marcos, TX 78666, USA

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 1, 69-81

Abstract: DNA methylation is an epigenetic modification involved in organism development and cellular differentiation. Identifying differential methylations can help to study genomic regions associated with diseases. Differential methylation studies on single-CG resolution have become possible with the bisulfite sequencing (BS) technology. However, there is still a lack of efficient statistical methods for identifying differentially methylated (DM) regions in BS data. We have developed a new approach named HMM-DM to detect DM regions between two biological conditions using BS data. This new approach first uses a hidden Markov model (HMM) to identify DM CG sites accounting for spatial correlation across CG sites and variation across samples, and then summarizes identified sites into regions. We demonstrate through a simulation study that our approach has a superior performance compared to BSmooth. We also illustrate the application of HMM-DM using a real breast cancer dataset.

Date: 2016
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1515/sagmb-2015-0077

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

Page updated 2021-05-07
Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:1:p:69-81:n:6