Abstract
Crosslinking and immunoprecipitation (CLIP) protocols have made it possible to identify transcriptome-wide RNA-protein interaction sites. In particular, PAR-CLIP utilizes a photoactivatable nucleoside for more efficient crosslinking. We present an approach, centered on the novel PARalyzer tool, for mapping high-confidence sites from PAR-CLIP deep-sequencing data. We show that PARalyzer delineates sites with a high signal-to-noise ratio. Motif finding identifies the sequence preferences of RNA-binding proteins, as well as seed-matches for highly expressed microRNAs when profiling Argonaute proteins. Our study describes tailored analytical methods and provides guidelines for future efforts to utilize high-throughput sequencing in RNA biology. PARalyzer is available at http://www.genome.duke.edu/labs/ohler/research/PARalyzer/.
Highlights
RNA binding proteins (RBPs) play important roles in the life cycle of a transcript, from its nascence by RNA polymerase until its decay by RNases
The fourth dataset consists of pooled libraries assaying members of the Argonaute (AGO) family of RBPs, central components of the RNA-induced silencing complex (RISC), which directs microRNAs to their target transcripts, thereby negatively impacting gene expression [19]
In addition to mapping sequence-specific RBPs such as PUM2, QKI or Insulin-like growth factor 2 binding protein 1 (IGF2BP1), an anticipated popular application of this protocol will be to study binding by members of the RISC, making it possible to identify the joint set of transcriptome-wide miRNA targets under specific conditions
Summary
RNA binding proteins (RBPs) play important roles in the life cycle of a transcript, from its nascence by RNA polymerase until its decay by RNases. Associated mRNAs are isolated, and quantified using mRNA arrays or, more recently, subjected to high-throughput sequencing This allows for the identification of all transcripts targeted by a particular RBP, but not for direct identification of where, or how many, RNA-protein interactions occur within a transcript. For the purposes of motif finding, current PAR-CLIP datasets fall into two distinct scenarios: (1) ‘single binding motif analysis’ in the case of sequence-specific RBPs (for example, QKI, PUM2, IFG2BP1); and (2) ‘multiple motif analysis’ in the special case of miRNA-mediated AGO-RNA crosslinking. For the single binding motif analysis we apply the conserved Evidence Ranked Motif Identification Tool (cERMIT) [8], which was designed for de novo motif discovery based on high-throughput binding data (for example, ChIP-seq) and has been shown to exhibit highly competitive performance in the context of transcription factor binding site discovery [8]. Rather than identifying a motif overrepresented in a pre-specified number of top candidate sequences, cERMIT ranks all putative target regions based on their binding evidence and identifies sequence motifs of flexible length that are highly enriched in targets with high binding evidence
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