摘要：SummaryIdentifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.Graphical abstractDisplay OmittedHighlights•Efficient motif anchoring helps to get good quality contextual information on binding•Realistic and high granularity datasets ensure better performance of the classifiers•DNN models on the contextual features outperform more complex deep learning tools•RBPSpot algorithm may be used to develop RBP binding models for other species alsoSequence analysis; Systems biology; Omics
