RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing

Genome Biology, Jan 2014

RNA-binding proteins (RBPs) regulate splicing according to position-dependent principles, which can be exploited for analysis of regulatory motifs. Here we present RNAmotifs, a method that evaluates the sequence around differentially regulated alternative exons to identify clusters of short and degenerate sequences, referred to as multivalent RNA motifs. We show that diverse RBPs share basic positional principles, but differ in their propensity to enhance or repress exon inclusion. We assess exons differentially spliced between brain and heart, identifying known and new regulatory motifs, and predict the expression pattern of RBPs that bind these motifs. RNAmotifs is available at https://bitbucket.org/rogrro/rna_motifs.

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RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing

Cereda et al. Genome Biology RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing Matteo Cereda 0 1 Uberto Pozzoli 1 Gregor Rot 0 Peter Juvan 0 Anthony Schweitzer Tyson Clark Jernej Ule 0 0 MRC Laboratory of Molecular Biology , Hills Road, Cambridge CB2 0QH , UK 1 Scientific Institute IRCCS E. Medea , Via don L. Monza 20, 23842 Bosisio Parini, (LC) , Italy RNA-binding proteins (RBPs) regulate splicing according to position-dependent principles, which can be exploited for analysis of regulatory motifs. Here we present RNAmotifs, a method that evaluates the sequence around differentially regulated alternative exons to identify clusters of short and degenerate sequences, referred to as multivalent RNA motifs. We show that diverse RBPs share basic positional principles, but differ in their propensity to enhance or repress exon inclusion. We assess exons differentially spliced between brain and heart, identifying known and new regulatory motifs, and predict the expression pattern of RBPs that bind these motifs. RNAmotifs is available at https://bitbucket.org/rogrro/rna_motifs. - Background The majority of human genes produce multiple mRNA isoforms via the process of alternative splicing [1]. Alternative splicing is regulated mainly by RNA-binding proteins (RBPs), which often act according to positional principles defined by an RNA splicing map to enhance or repress exon inclusion [2,3]. These RBPs play key roles in development and evolution, and mutations perturbing protein-RNA interactions can lead to a variety of diseases [4,5]. Therefore, to infer the splicing regulatory programs and identify new disease-causing mutations, algorithms are required that can assess the genomic sequence at the differentially regulated exons to predict the RNA motifs bound by these RBPs. Great progress has been made over the past decade in inferring the programs of splicing regulation [1]. However, it is not yet clear which positional principles of splicing regulation are shared between different RBPs. The sites of protein-RNA interactions have been defined by different crosslinking and immunoprecipitation (CLIP) methods (HITS-CLIP, PAR-CLIP or iCLIP), but the differences between these methods preclude precise comparisons between the RNA maps that were derived for the different RBPs [3]. Moreover, crosslinking-based methods are affected by mild sequence biases [6]; thus, it is important to develop methods that can derive the regulatory motifs independently of the CLIP data. Therefore, a new computational method is required to derive RNA maps solely from the analysis of gene expression data. Past studies that predicted splicing regulatory motifs from analysis of the differentially regulated exons searched for continuous motifs, which most often identified UGCAUG as the most frequent motif [7-15]. This sequence is recognized by RNA binding protein, fox-1 homologs 1 and 2 (RBFOX1 and RBFOX2), splicing regulators that recognize three nucleotides via the canonical RNA binding surface and an additional four nucleotides via the loops of a quasiRRM (qRRM) domain [16]. However, RBFOX proteins are exceptional in their ability to recognize a long continuous motif, and most other splicing regulators recognize motifs that are only three or four nucleotides long [17,18]. Studies of neuro-oncological ventral antigen 1 and 2 (NOVA1 and NOVA2), here collectively referred to as NOVA proteins, demonstrated that three or more short RNA motifs that are clustered closely together on the pre-mRNA are required for NOVA proteins to mediate splicing regulation [2]. Here we will refer to these motifs as 'multivalent RNA motifs', since they enable RBPs to achieve high-affinity binding by cooperative interactions between multiple RNA-binding domains and the clustered short RNA motifs [17,18]. Past computational methods for analysis of multivalent RNA motifs have focused on the known RNA motifs [19], or have predicted motifs based on the CLIP studies of protein-RNA interactions [17,18]. However, a method for de novo identification of multivalent RNA motifs by analysis of the regulated exons is not yet available. Here, we present RNAmotifs, a method that identifies clusters of short non-degenerate (ND) or degenerate (DG) tetramers that are enriched at specific positions around the enhanced and silenced exons. The method correctly identified the multivalent RNA motifs bound by NOVA, PTBP1, heterogeneous nuclear ribonucleoprotein C (hnRNP C), TARDBP, and TIA1 and TIAL1 cytotoxic granule-associated RNA binding proteins (here collectively referred to as TIA proteins). Moreover, RNAmotifs determines the RNA splicing map, which enabled us to compare the positional principles of different RBPs. Finally, we analyzed the exons that are differentially spliced between brain and heart, identifying new candidate motifs responsible for tissue-specific splicing regulation. Notably, we demonstrate that the positional enrichment informatio (...truncated)


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Matteo Cereda, Uberto Pozzoli, Gregor Rot, Peter Juvan, Anthony Schweitzer, Tyson Clark, Jernej Ule. RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing, Genome Biology, 2014, pp. R20, 15, DOI: 10.1186/gb-2014-15-1-r20