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ModHMM: a modular supra-Bayesian genome segmentation method. (English) Zbl 1412.92192
Cowen, Lenore J. (ed.), Research in computational molecular biology. 23rd annual international conference, RECOMB 2019, Washington, DC, USA, May 5–8, 2019. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11467, 35-50 (2019).
Summary: 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.
For the entire collection see [Zbl 1408.92004].
MSC:
92D10 Genetics and epigenetics
92-08 Computational methods for problems pertaining to biology
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