Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data
| LEADER | naa a22 4500 | ||
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| 001 | 528784714 | ||
| 005 | 20180924065517.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 180924e201502 xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.3929/ethz-b-000110087 |2 doi |
| 024 | 7 | 0 | |a 10.1186/s12859-015-0470-y |2 doi |
| 035 | |a (ETHRESEARCH)oai:www.research-collecti.ethz.ch:20.500.11850/110087 | ||
| 100 | 1 | |a Comoglio |D Federico | |
| 245 | 1 | 0 | |a Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data |h [Elektronische Daten] |c [Federico Comoglio, Cem Sievers, Renato Paro] |
| 246 | 0 | |a BMC bioinformatics | |
| 506 | |a Open access |2 ethresearch | ||
| 520 | 3 | |a 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. | |
| 540 | |a Creative Commons Attribution 4.0 International |u http://creativecommons.org/licenses/by/4.0 |2 ethresearch | ||
| 690 | 7 | |a PAR-CLIP |2 ethresearch | |
| 690 | 7 | |a RNA |2 ethresearch | |
| 690 | 7 | |a RNA binding proteins |2 ethresearch | |
| 690 | 7 | |a Bayesian statistics |2 ethresearch | |
| 700 | 1 | |a Sievers |D Cem |e joint author | |
| 700 | 1 | |a Paro |D Renato |e joint author | |
| 773 | 0 | |t BMC Bioinformatics |d London : BioMed Central |g 16, p. 32 |x 1471-2105 | |
| 856 | 4 | 0 | |u http://hdl.handle.net/20.500.11850/110087 |q text/html |z WWW-Backlink auf das Repository (Open access) |
| 908 | |D 1 |a Journal Article |2 ethresearch | ||
| 950 | |B ETHRESEARCH |P 856 |E 40 |u http://hdl.handle.net/20.500.11850/110087 |q text/html |z WWW-Backlink auf das Repository (Open access) | ||
| 950 | |B ETHRESEARCH |P 100 |E 1- |a Comoglio |D Federico | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Sievers |D Cem |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Paro |D Renato |e joint author | ||
| 950 | |B ETHRESEARCH |P 773 |E 0- |t BMC Bioinformatics |d London : BioMed Central |g 16, p. 32 |x 1471-2105 | ||
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B ETHRESEARCH |F ETHRESEARCH |b ETHRESEARCH |j Journal Article |c Open access | ||