4.5 Article

omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data

Journal

GENOME BIOLOGY
Volume 19, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/s13059-018-1521-2

Keywords

Machine learning; Bioinformatics; Protein-RNA interactions; CLIP-seq; eCLIP; iCLIP; PAR-CLIP; HITS-CLIP; Generalized linear models; Mixture models

Funding

  1. DFG [OH266/2-1]
  2. US National Institutes of Health [R01-GM104962]
  3. Bundesministerium fur Bildung und Forschung under grant CaRNAtion
  4. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM104962] Funding Source: NIH RePORTER

Ask authors/readers for more resources

CLIP-seq methods allow the generation of genome-wide maps of RNA binding protein - RNA interaction sites. However, due to differences between different CLIP-seq assays, existing computational approaches to analyze the data can only be applied to a subset of assays. Here, we present a probabilistic model called omniCLIP that can detect regulatory elements in RNAs from data of all CLIP-seq assays. omniCLIP jointly models data across replicates and can integrate background information. Therefore, omniCLIP greatly simplifies the data analysis, increases the reliability of results and paves the way for integrative studies based on data from different assays.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available