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Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models
Max-Delbrück-Center for Molecular Medicine, Germany.
2014 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 42, no 21, 12995-13011 p.Article in journal (Refereed) Published
Abstract [en]

We present a discriminative learning method for pattern discovery of binding sites in nucleic acid sequences based on hidden Markov models. Sets of positive and negative example sequences are mined for sequence motifs whose occurrence frequency varies between the sets. The method offers several objective functions, but we concentrate on mutual information of condition and motif occurrence. We perform a systematic comparison of our method and numerous published motif-finding tools. Our method achieves the highest motif discovery performance, while being faster than most published methods. We present case studies of data from various technologies, including ChIP-Seq, RIP-Chip and PAR-CLIP, of embryonic stem cell transcription factors and of RNA-binding proteins, demonstrating practicality and utility of the method. For the alternative splicing factor RBM10, our analysis finds motifs known to be splicing-relevant. The motif discovery method is implemented in the free software package Discrover. It is applicable to genome- and transcriptome-scale data, makes use of available repeat experiments and aside from binary contrasts also more complex data configurations can be utilized.

Place, publisher, year, edition, pages
Oxford University Press, 2014. Vol. 42, no 21, 12995-13011 p.
National Category
Bioinformatics and Systems Biology
URN: urn:nbn:se:kth:diva-178102DOI: 10.1093/nar/gku1083ISI: 000347914600011PubMedID: 25389269ScopusID: 2-s2.0-84945241769OAI: diva2:877600

QC 20151208

Available from: 2015-12-07 Created: 2015-12-07 Last updated: 2015-12-08Bibliographically approved

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Maaskola, Jonas
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