  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.
