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Genome Biol. 2017 Sep 8;18(1):169. doi: 10.1186/s13059-017-1298-8.

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

Author information

1
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
2
Life Sciences Institute, Innovation Center for Cell Signaling Network, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
3
European Molecular Biology Laboratory, Grenoble Outstation, 71 Avenue des Martyrs, Grenoble, 38042, France.
4
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China. zhilu@tsinghua.edu.cn.

Abstract

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.

KEYWORDS:

CLIP-seq; Non-negative matrix factorization; RBPgroup; RNA-binding protein

PMID:
28886744
PMCID:
PMC5591525
DOI:
10.1186/s13059-017-1298-8
[Indexed for MEDLINE]
Free PMC Article

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