A Model-based Approach to Identify Binding Sites in CLIP-Seq Data
Overview
Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present MiClip,a novel model-based approach to identify high-confidence protein-RNA binding sites in CLIP-Seq datasets. This approach assigns confidence value to each binding site on a probabilistic basis. The MiClip package can be flexibly applied to analyze both HITS-CLIP data and PAR-CLIP data.
- Fixed a bug in removing suffix from paired-end SAM files
- Now MiClip recognizes "~" as the home directory
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- First published
- 2013-02-27
- Total releases
- 3 / 13 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 2.15.0
- Bundled data
- 100 KB / 2 files
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