Probabilistic modeling of occurring substitutions in PAR-CLIP data

TL;DR

BMix is a probabilistic method that improves the detection of true T-to-C substitutions in PAR-CLIP data by accounting for noise, leading to more accurate identification of RNA-protein interaction sites.

Contribution

Introduces BMix, a novel probabilistic approach that explicitly models noise in PAR-CLIP data to distinguish true from false T-to-C substitutions.

Findings

Outperforms existing methods in speed and accuracy

Effective on both simulated and real datasets

Provides a robust statistical framework for PAR-CLIP analysis

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 to 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 to existing approaches on both simulated and real, publicly available human datasets. The model is implemented in the Matlab toolbox BMix, freely available at www.cbg.bsse.ethz.ch/software/BMix.