title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data.
abstract: BACKGROUND
  PAR-CLIP is a recently developed Next Generation Sequencing-based method enabling transcriptome-wide identification of interaction sites between RNA and RNA-binding proteins. The PAR-CLIP procedure induces specific base transitions that originate from sites of RNA-protein interactions and can therefore guide the identification of binding sites. However, additional sources of transitions, such as cell type-specific SNPs and sequencing errors, challenge the inference of binding sites and suitable statistical approaches are crucial to control false discovery rates. In addition, a highly resolved delineation of binding sites followed by an extensive downstream analysis is necessary for a comprehensive characterization of the protein binding preferences and the subsequent design of validation experiments.
  
  
  RESULTS
  We present a statistical and computational framework for PAR-CLIP data analysis. We developed a sensitive transition-centered algorithm specifically designed to resolve protein binding sites at high resolution in PAR-CLIP data. Our method employes a Bayesian network approach to associate posterior log-odds with the observed transitions, providing an overall quantification of the confidence in RNA-protein interaction. We use published PAR-CLIP data to demonstrate the advantages of our approach, which compares favorably with alternative algorithms. Lastly, by integrating RNA-Seq data we compute conservative experimentally-based false discovery rates of our method and demonstrate the high precision of our strategy.
  
  
  CONCLUSIONS
  Our method is implemented in the R package wavClusteR 2.0. The package is distributed under the GPL-2 license and is available from BioConductor at http://www.bioconductor.org/packages/devel/bioc/html/wavClusteR.html .
authors: Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, Mattenstrasse 26, Basel, 4058, Switzerland. federico.comoglio@bsse.ethz.ch.
authors: Federico
authors: F
authors: Comoglio
authors: Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, Mattenstrasse 26, Basel, 4058, Switzerland. csievers@mgh.harvard.edu.|Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, USA. csievers@mgh.harvard.edu.|Broad Institute of MIT and Harvard, Cambridge, USA. csievers@mgh.harvard.edu.
authors: Cem
authors: C
authors: Sievers
authors: Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, Mattenstrasse 26, Basel, 4058, Switzerland. renato.paro@bsse.ethz.ch.|Faculty of Science, University of Basel, Klingelbergstrasse 504056, Basel, Switzerland. renato.paro@bsse.ethz.ch.
authors: Renato
authors: R
authors: Paro
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001665:Binding Sites; D057809:HEK293 Cells; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D047468:Immunoprecipitation; D035683:MicroRNAs / Q000737:chemistry / Q000378:metabolism*; D015233:Models, Statistical; D012313:RNA / Q000737:chemistry / Q000378:metabolism*; D016601:RNA-Binding Proteins / Q000378:metabolism*; D017423:Sequence Analysis, RNA / Q000379:methods*; D059467:Transcriptome
chemical_list: D035683:MicroRNAs; D016601:RNA-Binding Proteins; D012313:RNA
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