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Introduction：

RNA-protein binding sites prediction suite based on deep learning

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RNA-binding proteins (RBPs) are involved in many biological processes, their binding sites on RNAs can give insights into mechanisms behind diseases involving RBPs. Thus, how to identify the RBP binding sites on RNAs is very crucial for follow-up analysis, like the impact of mutations on binding sites.

With high-throughput sequencing developing, there is an explosion in the amount of experimentally verified RBP binding sites, e.g. eCLIP in ENCODE. However,CLIP-seq to detect RBP binding sites relies on gene expression which can be highly variable between experiments, and cannot provide a complete picture of the RBP binding landscape. Computational methods are in urgent needed to predict missing binding sites for individual RBPs.

Considering that RBPs have difference binding preferences, the machine leaning-based methods train protein-specific models; each model is trained per RBP. In addition, RBP can bind to both linear RNAs and circular RNAs (circRNAs), and RBP may show different binding preference to linear RNAs and circRNAs.

In RBPsuite, we mainly contain two deep learning-based approaches, iDeepS and CRIP . iDeepS is deveoped for predicting RBP binding sites on linear RNAs. CRIP is developed for predicting RBP binding sites on circular RNAs.