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Nucleic Acids Res. 2013 Aug;41(14):6793-807. doi: 10.1093/nar/gkt421. Epub 2013 May 18.

Prediction of clustered RNA-binding protein motif sites in the mammalian genome.

Author information

1
Laboratory of Molecular Neuro-Oncology, Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. cz2294@columbia.edu

Abstract

Sequence-specific interactions of RNA-binding proteins (RBPs) with their target transcripts are essential for post-transcriptional gene expression regulation in mammals. However, accurate prediction of RBP motif sites has been difficult because many RBPs recognize short and degenerate sequences. Here we describe a hidden Markov model (HMM)-based algorithm mCarts to predict clustered functional RBP-binding sites by effectively integrating the number and spacing of individual motif sites, their accessibility in local RNA secondary structures and cross-species conservation. This algorithm learns and quantifies rules of these features, taking advantage of a large number of in vivo RBP-binding sites obtained from cross-linking and immunoprecipitation data. We applied this algorithm to study two representative RBP families, Nova and Mbnl, which regulate tissue-specific alternative splicing through interacting with clustered YCAY and YGCY elements, respectively, and predicted their binding sites in the mouse transcriptome. Despite the low information content in individual motif elements, our algorithm made specific predictions for successful experimental validation. Analysis of predicted sites also revealed cases of extensive and distal RBP-binding sites important for splicing regulation. This algorithm can be readily applied to other RBPs to infer their RNA-regulatory networks. The software is freely available at http://zhanglab.c2b2.columbia.edu/index.php/MCarts.

PMID:
23685613
PMCID:
PMC3737533
DOI:
10.1093/nar/gkt421
[Indexed for MEDLINE]
Free PMC Article

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