RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery

Abstract

Identifying 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.