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ModHMM: A Modular Supra-Bayesian Genome Segmentation Method

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Research in Computational Molecular Biology (RECOMB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11467))

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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Acknowledgements

We thank Anna Ramisch, Tobias Zehnder, and Verena Heinrich for their comments on the manuscript and many inspiring discussions.

PB was supported by the German Ministry of Education and Research (BMBF, grant no. 01IS18037G).

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Authors and Affiliations

  1. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 73, 14195, Berlin, Germany

    Philipp Benner & Martin Vingron

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  1. Philipp Benner
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  2. Martin Vingron
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Correspondence to Philipp Benner .

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Editors and Affiliations

  1. Tufts University, Cambridge, MA, USA

    Lenore J. Cowen

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Benner, P., Vingron, M. (2019). ModHMM: A Modular Supra-Bayesian Genome Segmentation Method. In: Cowen, L. (eds) Research in Computational Molecular Biology. RECOMB 2019. Lecture Notes in Computer Science(), vol 11467. Springer, Cham. https://doi.org/10.1007/978-3-030-17083-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-17083-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17082-0

  • Online ISBN: 978-3-030-17083-7

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