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2019 | OriginalPaper | Buchkapitel

ModHMM: A Modular Supra-Bayesian Genome Segmentation Method

verfasst von : Philipp Benner, Martin Vingron

Erschienen in: Research in Computational Molecular Biology

Verlag: Springer International Publishing

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Abstract

Genome segmentation methods are powerful tools to obtain cell type or tissue specific genome-wide annotations and are frequently used to discover regulatory elements. However, traditional segmentation methods show low predictive accuracy and their data-driven annotations have some undesirable properties. As an alternative, we developed ModHMM, a highly modular genome segmentation method. Inspired by the supra-Bayesian approach, it incorporates predictions from a set of classifiers. This allows to compute genome segmentations by utilizing state-of-the-art methodology. We demonstrate the method on ENCODE data and show that it outperforms traditional segmentation methods not only in terms of predictive performance, but also in qualitative aspects. Therefore, ModHMM is a valuable alternative to study the epigenetic and regulatory landscape across and within cell types or tissues. The software is freely available at https://github.com/pbenner/modhmm.

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Titel: ModHMM: A Modular Supra-Bayesian Genome Segmentation Method
verfasst von: Philipp Benner
Martin Vingron
Verlag: Springer International Publishing
Buch: Research in Computational Molecular Biology
Print ISBN: 978-3-030-17082-0

Electronic ISBN: 978-3-030-17083-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17083-7_3

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