Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites

• Published in: Genome Biology 2017-09, Vol.18 (1), p.169-169, Article 169
• Main Authors: Li, Yang Eric, Xiao, Mu, Shi, Binbin, Yang, Yu-Cheng T, Wang, Dong, Wang, Fei, Marcia, Marco, Lu, Zhi John
• Format: Article
• Language: English
• Subjects: Binding Sites; Bioinformatics; Biosynthesis; Cell lines; CLIP-seq; Competition; crosslinking; data collection; Datasets; Gene expression; Genomes; HEK293 Cells; Hep G2 Cells; Humans; Immunoprecipitation; K562 Cells; Metabolism; Method; Mutation; Non-negative matrix factorization; Nucleotide Motifs; precipitin tests; Protein Binding; Proteins; RBPgroup; Regulatory sequences; Regulatory Sequences, Nucleic Acid; researchers; Ribonucleic acid; RNA; RNA - metabolism; RNA-binding protein; RNA-binding proteins; RNA-Binding Proteins - classification; RNA-Binding Proteins - metabolism; Sequence Analysis, RNA - methods; Software
• ISSN: 1474-760X; 1474-7596; 1465-6906; 1474-760X
• DOI: 10.1186/s13059-017-1298-8

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.