Improving CLIP-seq data analysis by incorporating transcript information
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RESEARCH ARTICLE
Open Access
Improving CLIP-seq data analysis by incorporating transcript information Michael Uhl1 , Van Dinh Tran1 and Rolf Backofen1,2* Abstract Background: Current peak callers for identifying RNA-binding protein (RBP) binding sites from CLIP-seq data take into account genomic read profiles, but they ignore the underlying transcript information, that is information regarding splicing events. So far, there are no studies available that closer observe this issue. Results: Here we show that current peak callers are susceptible to false peak calling near exon borders. We quantify its extent in publicly available datasets, which turns out to be substantial. By providing a tool called CLIPcontext for automatic transcript and genomic context sequence extraction, we further demonstrate that context choice affects the performances of RBP binding site prediction tools. Moreover, we show that known motifs of exon-binding RBPs are often enriched in transcript context sites, which should enable the recovery of more authentic binding sites. Finally, we discuss possible strategies on how to integrate transcript information into future workflows. Conclusions: Our results demonstrate the importance of incorporating transcript information in CLIP-seq data analysis. Taking advantage of the underlying transcript information should therefore become an integral part of future peak calling and downstream analysis tools. Keywords: CLIP-seq, eCLIP, Peak calling, RBP binding site prediction
Background Over the last decade, CLIP-seq (cross-linking and immunoprecipitation followed by next generation sequencing) [1] has become the state-of-the-art procedure to experimentally determine the precise transcriptome-wide binding locations of RNA-binding proteins (RBPs). Many variants have been introduced, out of which PAR-CLIP [2], iCLIP [3], and eCLIP [4] are currently the most widely used. Regardless of the variant, CLIP-seq is usually applied in vivo to a specific RBP, producing a library of reads bound by the RBP. Identification of binding sites is subsequently achieved by mapping the reads back to the corresponding reference genome and running a so called peak caller tool on the read profiles. A number of popular *Correspondence: [email protected] Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany 2 Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany 1
peak callers have emerged over the years, such as Piranha [5], CLIPper [6], PEAKachu [7], and PureCLIP [8]. While there exist various protocol-specific as well as more generic peak callers [9], none of the current tools takes into account the transcript information underlying the mapped reads. Instead, they extract binding regions directly from the genomic read profiles. This can be acceptable if the studied RBP binds intronic sequences or in general unspliced RNAs. However, if the RBP is actually predominantly binding to
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