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  ModHMM: A Modular Supra-Bayesian Genome Segmentation Method

Benner, P., & Vingron, M. (2019). ModHMM: A Modular Supra-Bayesian Genome Segmentation Method. Journal of Computational Biology, 27. doi:10.1089/cmb.2019.0280.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-0A00-F Version Permalink: http://hdl.handle.net/21.11116/0000-0006-0A03-C
Genre: Journal Article

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Benner_2019.pdf (Publisher version), 721KB
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© 2020 Mary Ann Liebert, Inc.
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 Creators:
Benner, Philipp1, Author              
Vingron, Martin1, Author              
Affiliations:
1Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Free keywords: Hmm genome segmentation supra-Bayesian
 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.

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Language(s): eng - English
 Dates: 2019-12-18
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1089/cmb.2019.0280
ISSN: 1557-8666 (Electronic)1066-5277 (Print)
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Title: Journal of Computational Biology
Source Genre: Journal
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Publ. Info: New York, NY : Mary Ann Liebert
Pages: 16 Volume / Issue: 27 Sequence Number: - Start / End Page: - Identifier: ISSN: 1066-5277
CoNE: https://pure.mpg.de/cone/journals/resource/954925275499