A Model-Based Approach to Identify Binding Sites in CLIP-Seq Data

<div><p>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 <i>ad hoc</i> 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 <i>ad hoc</i> method and two recently published methods. To facilitate the application of the algorithm, we have released an R package, <i>MiClip (</i><a href="http://cran.r-project.org/web/packages/MiClip/index.html" target="_blank">http://cran.r-project.org/web/packages/MiClip/index.html</a><i>)</i>, and a public web-based graphical user interface software (<a href="http://galaxy.qbrc.org/tool_runner?tool_id=mi_clip" target="_blank">http://galaxy.qbrc.org/tool_runner?tool_id=mi_clip</a>) for customized analysis.</p>