Journal
BMC GENOMICS
Volume 21, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s12864-020-07291-6
Keywords
Deep learning; RNA-binding proteins; Linear RNAs; Circular RNAs
Funding
- National Key Research and Development Program of China [2018YFC0910500]
- National Natural Science Foundation of China [61903248, 61725302, 61671288]
- Science and Technology Commission of Shanghai Municipality [17JC1403500, 20S11902100]
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BackgroundRNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive.ResultsHere we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence.ConclusionsRBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/.
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