A model-based approach to identify binding sites in CLIP-seq data

Tao Wang, Beibei Chen, MinSoo Kim, Yang Xie, Guanghua Xiao

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present a novel model-based approach (MiClip) to identify high-confidence protein-RNA binding sites from CLIP-seq datasets. This approach assigns a probability score for each potential binding site to help prioritize subsequent validation experiments. The MiClip algorithm has been tested in both HITS-CLIP and PAR-CLIP datasets. In the HITS-CLIP dataset, the signal/noise ratios of miRNA seed motif enrichment produced by the MiClip approach are between 17% and 301% higher than those by the ad hoc method for the top 10 most enriched miRNAs. In the PAR-CLIP dataset, the MiClip approach can identify ∼50% more validated binding targets than the original ad hoc method and two recently published methods. To facilitate the application of the algorithm, we have released an R package, MiClip (http://cran.r-project.org/web/packages/ MiClip/index.html ) , and a public web-based graphical user interface software ( http://galaxy.qbrc.org/ tool-runner?tool-id=mi-clip) for customized analysis.

Original languageEnglish (US)
Article numbere93248
JournalPloS one
Volume9
Issue number4
DOIs
StatePublished - Apr 8 2014

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ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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