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

Chaolin Zhang*, Kuang Yung Lee, Maurice S. Swanson, Robert B. Darnell

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

54 Scopus citations


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 crosslinking and immunoprecipitation data. We applied this algorithm to study two representative RBP families, Nova and Mbnl, which regulate tissuespecific 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 RNAregulatory networks. The software is freely available at MCarts.

Original languageEnglish
Pages (from-to)6793-6807
Number of pages15
JournalNucleic Acids Research
Issue number14
StatePublished - 08 2013
Externally publishedYes


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