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

• Published in: Genome Biology Vol. 18; no. 1; p. 169
• Main Authors: Li, Yang Eric, Xiao, Mu, Shi, Binbin, Yang, Yu-Cheng T, Wang, Dong, Wang, Fei, Marcia, Marco, Lu, Zhi John
• Format: Journal Article
• Language: English
• Published: England BioMed Central 08-09-2017; BMC
• Subjects: Binding Sites; Bioinformatics; Biosynthesis; Cell lines; CLIP-seq; Competition; Datasets; Gene expression; Genomes; HEK293 Cells; Hep G2 Cells; Humans; Immunoprecipitation; K562 Cells; Metabolism; Method; Mutation; Non-negative matrix factorization; Nucleotide Motifs; Protein Binding; Proteins; RBPgroup; Regulatory sequences; Regulatory Sequences, Nucleic Acid; Ribonucleic acid; RNA; RNA - metabolism; RNA-binding protein; RNA-Binding Proteins - classification; RNA-Binding Proteins - metabolism; Sequence Analysis, RNA - methods; Software; RNA-binding protein; Non-negative matrix factorization; CLIP-seq; RBPgroup
• 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.