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

Sequence-specific interactions of RNA-binding proteins (RBPs) with their target transcripts are es-sential for post-transcriptional gene expression regulation in mammals. However, accurate predic-tion of RBP motif sites has been difficult because many RBPs recognize short and degenerate se-quences. 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 second-ary 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, respect-ively, 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 val-idation. Analysis of predicted sites also revealed cases of extensive and distal RBP-binding sites im-portant for splicing regulation. This algorithm can be readily applied to other RBPs to infer their RNA-regulatory networks. The software is freely avail-able a