BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data

Bioinformatics
Monica GolumbeanuNiko Beerenwinkel

Abstract

Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations. Therefore, rigorous statistical analysis is required in order to separate true T-to-C base changes, following cross-linking, from noise. So far, most of the existing PAR-CLIP data analysis methods focus on discarding the low-frequency errors and rely on high-frequency substitutions to report binding sites, not taking into account the possibility of high-frequency false positive substitutions. Here, we introduce BMix, a new probabilistic method which explicitly accounts for the sources of noise in PAR-CLIP data and distinguishes cross-link induced T-to-C substitutions from low and high-frequency erroneous alterations. We demonstrate the superior speed and accuracy of our method compared with existing approaches on both s...Continue Reading

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Dec 23, 2016·Wiley Interdisciplinary Reviews. RNA·Supriyo De, Myriam Gorospe
Nov 18, 2017·Nature Communications·Stefan J SiiraAleksandra Filipovska
Nov 13, 2018·Bioinformatics·Eva-Maria HuesslerPablo Landgraf
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Nov 5, 2016·PeerJ·Andreas KloetgenAlice C McHardy
Jun 27, 2019·Cell Reports·Riccardo De SantisAlessandro Rosa

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