naa a22 4500 528784714 20180924065517.0 cr unu---uuuuu 180924e201502 xx s 000 0 eng 10.3929/ethz-b-000110087 doi 10.1186/s12859-015-0470-y doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/110087 Comoglio Federico Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data [Elektronische Daten] [Federico Comoglio, Cem Sievers, Renato Paro] BMC bioinformatics Open access ethresearch 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. Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 ethresearch PAR-CLIP ethresearch RNA ethresearch RNA binding proteins ethresearch Bayesian statistics ethresearch Sievers Cem joint author Paro Renato joint author BMC Bioinformatics London : BioMed Central 16, p. 32 1471-2105 http://hdl.handle.net/20.500.11850/110087 text/html WWW-Backlink auf das Repository (Open access) 1 Journal Article ethresearch ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/110087 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 100 1- Comoglio Federico ETHRESEARCH 700 1- Sievers Cem joint author ETHRESEARCH 700 1- Paro Renato joint author ETHRESEARCH 773 0- BMC Bioinformatics London : BioMed Central 16, p. 32 1471-2105 BK010053 XK010053 XK010000 ETHRESEARCH ETHRESEARCH ETHRESEARCH Journal Article Open access