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  ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data

Heller, D., Krestel, R., Ohler, U., Vingron, M., & Marsico, A. (2017). ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data. Nucleic Acids Research (London), 45(19), 11004-11018. doi:10.1093/nar/gkx756.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-8153-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-8154-F
Genre: Journal Article

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 Creators:
Heller, D.1, Author              
Krestel, R., Author
Ohler, U., Author
Vingron, M.2, Author              
Marsico, A.3, Author              
Affiliations:
1IMPRS for Computational Biology and Scientific Computing - IMPRS-CBSC (Kirsten Kelleher), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              
3RNA Bioinformatics (Annalisa Marsico), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2117285              

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 Abstract: RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM's model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image.

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Language(s): eng - English
 Dates: 2017-08-172017-08-302017-11-02
 Publication Status: Published in print
 Pages: 15
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 Identifiers: DOI: 10.1093/nar/gkx756
ISSN: 1362-4962 (Electronic)0305-1048 (Print)
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Title: Nucleic Acids Research (London)
  Other : Nucleic Acids Res
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 45 (19) Sequence Number: - Start / End Page: 11004 - 11018 Identifier: ISSN: 0305-1048
CoNE: /journals/resource/110992357379342