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

• Published in: Genome Biology Vol. 19; no. 1; p. 183
• Main Authors: Drewe-Boss, Philipp, Wessels, Hans-Hermann, Ohler, Uwe
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.11.2018; BMC
• Subjects: Animal Genetics and Genomics; Binding Sites; Bioinformatics; Biomedical and Life Sciences; CLIP-seq; Computational Biology - methods; Computer applications; Computer Simulation; Data analysis; Data processing; eCLIP; Evolutionary Biology; Gene expression; Gene mapping; Genetic engineering; genome; Genomes; High-Throughput Nucleotide Sequencing - methods; Human Genetics; Humans; iCLIP; Immunoprecipitation - methods; Information processing; Life Sciences; Machine learning; Method; Microbial Genetics and Genomics; Plant Genetics and Genomics; probabilistic models; Protein-RNA interactions; Proteins; Regulatory sequences; Ribonucleic acid; RNA; RNA - genetics; RNA - metabolism; RNA-binding protein; RNA-Binding Proteins - genetics; RNA-Binding Proteins - metabolism; Sequence Analysis, RNA; Software; HITS-CLIP; CLIP-seq; PAR-CLIP; Machine learning; Mixture models; eCLIP; Bioinformatics; Protein-RNA interactions; iCLIP; Generalized linear models
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-018-1521-2

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.