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Differential Methylation detection using a hierarchical Bayesian model exploiting Local Dependency DM-BLD


Detectes differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation changes in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables.

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DM-BLD classification

DM-BLD specifications

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Command line interface
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Unix/Linux, Mac OS, Windows
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DM-BLD distribution


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DM-BLD support



  • Jianhua Xuan <>


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Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA; Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, WA, DC, USA

Funding source(s)

This work was supported in part by the National Institutes of Health (CA149653, CA149147, CA184902, CA164384).

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