The HMM-DM program identifies differentially methylated (DM) CG sites and regions from whole genome and targeted bisulfite sequencing (BS) data. This approach first uses a Hidden Markov Model to identify differentially methylated CG sites accounting for spatial correlation across CGs and variation across samples, and then summarizes identified DM CG sites into regions based on their status and distance. This program takes aligned BS data in multiple samples and outputs identified DM CG sites and regions.

HMM-DM requires R installed. Ideally it is run in a Linux/Unix system. This program includes the following documents and folders:

HMM.DM.user.manual.pdf: A copy of the user manual

HMM.DM.suppl.pdf: A copy of the HMM-DM supplementary file

HMM.DM.code: A folder containing all R source code files used for HMM-DM.

example.data: A folder containing all example input data, an example.script.txt for running HMM-DM, and the output files generated from the example.script.txt.

Simulation: A folder containing all R scource code used for data simulating.

How to cite us

Yu, X. & Sun, S. (2016). HMM-DM: identifying differentially methylated regions using a hidden Markov model. Statistical Applications in Genetics and Molecular Biology, Doi:10.1515/sagmb-2015-0077

Contact us

Shuying Sun ([email protected]) and Xiaoqing Yu ([email protected])

Access to the published manuscripts

HMM-DM: http://www.ncbi.nlm.nih.gov/pubmed/26887041

HMM-Fisher: http://www.ncbi.nlm.nih.gov/pubmed/26854292

Comparing five DM methods: http://www.ncbi.nlm.nih.gov/pubmed/26910753