A model-based approach to identify binding sites in CLIP-Seq data.
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.
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Tao
T
Wang
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Beibei
B
Chen
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
MinSoo
M
Kim
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Yang
Y
Xie
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Guanghua
G
Xiao
D000465:Algorithms; D001665:Binding Sites; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D035683:MicroRNAs / Q000737:chemistry / Q000378:metabolism; D008954:Models, Biological; D012313:RNA / Q000737:chemistry / Q000378:metabolism*; D016601:RNA-Binding Proteins / Q000378:metabolism*; D017423:Sequence Analysis, RNA / Q000379:methods
D035683:MicroRNAs; D016601:RNA-Binding Proteins; D012313:RNA