General Information
| Description | Probabilistic modeling of occurring substitutions in PAR-CLIP data |
| Homepage | http://www.cbg.bsse.ethz.ch/software/BMix |
Publications
BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data
Motivation
: 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.
Results
: 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 simulated and real, publicly available human datasets.
Availability and implementation
: The model is freely accessible within the BMix toolbox at www.cbg.bsse.ethz.ch/software/BMix , available for Matlab and R.
Supplementary information:
Supplementary data is available at Bioinformatics online.
Contact
: niko.beerenwinkel@bsse.ethz.ch
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