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

Nitesh Kumar Sharma; Sagar Gupta; Ashwani Kumar; Prakash Kumar; Upendra Kumar Pradhan; Ravi Shankar

doi:10.1016/j.isci.2021.103381

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

iScience. 2021 Oct 30;24(12):103381. doi: 10.1016/j.isci.2021.103381. eCollection 2021 Dec 17.

Authors

Nitesh Kumar Sharma^{1

2}, Sagar Gupta¹, Ashwani Kumar¹, Prakash Kumar^{1

2

3}, Upendra Kumar Pradhan^{1

2

3}, Ravi Shankar^{1

2}

Affiliations

¹ Studio of Computational Biology & Bioinformatics (Biotech Division), The Himalayan Centre for High-throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT, India), CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, India.
² Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, Uttar Pradesh 201 002, India.
³ ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, Delhi 110012, India.

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.

Keywords: Omics; Sequence analysis; Systems biology.