Abstract
Summary: Here we introduce catRAPID omics, a server for large-scale calculations of protein–RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition.Results: We developed a web server to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis (custom libraries can be also generated). The catRAPID omics was benchmarked on the recently published RNA interactomes of Serine/arginine-rich splicing factor 1 (SRSF1), Histone-lysine N-methyltransferase EZH2 (EZH2), TAR DNA-binding protein 43 (TDP43) and RNA-binding protein FUS (FUS) as well as on the protein interactomes of U1/U2 small nucleolar RNAs, X inactive specific transcript (Xist) repeat A region (RepA) and Crumbs homolog 3 (CRB3) 3′-untranslated region RNAs. Our predictions are highly significant (P < 0.05) and will help the experimentalist to identify candidates for further validation.Availability: catRAPID omics can be freely accessed on the Web at http://s.tartaglialab.com/catrapid/omics. Documentation, tutorial and FAQs are available at http://s.tartaglialab.com/page/catrapid_group.Contact: gian.tartaglia@crg.eu
Highlights
Increasing evidence indicates that ribonucleoprotein interactions are fundamental for cellular regulation (Khalil and Rinn, 2011)
RPISeq is based on support vector machine (SVM) and random forest (RF)
We introduce catRAPID omics to perform high-throughput predictions of protein–RNA interactions using the information on protein and RNA domains involved in macromolecular recognition
Summary
Increasing evidence indicates that ribonucleoprotein interactions are fundamental for cellular regulation (Khalil and Rinn, 2011). There are two sequence-based methods for prediction of protein–RNA interactions: catRAPID (Bellucci et al, 2011) and RPISeq (Muppirala et al, 2011). The catRAPID algorithm exploits predictions of secondary structure, hydrogen bonding and van der Waals’ contributions to estimate the binding propensity of protein and RNA molecules. Models predicting protein–RNA interactions from primary structure alone (Muppirala et al, 2011). Both methods show remarkable performances, but catRAPID discriminates positive and negative cases with higher accuracy (Cirillo et al, 2013b) and has been tested on long non-coding RNAs (Agostini et al, 2013). We introduce catRAPID omics to perform high-throughput predictions of protein–RNA interactions using the information on protein and RNA domains involved in macromolecular recognition
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