A Model-based Approach to Identify Binding Sites in CLIP-Seq Data
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 the MiClip R package,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/PAR-CLIP data in single-end/paired-end format.
MiClip_1.0.tgz : The package binary for Mac OS
MiClip_1.0.zip : The package binary for Windows
MiClip_vignette.pdf : Vignette for MiClip
MiClip-manual.pdf : User manual for MiClip
Note: Please download the vignette for more detailed information. The source code will be provided upon publication of this study.
Version: 1.0
Date: 2012-11-15
Author: Tao Wang
Maintainer: Tao Wang [email protected]
Depends: R (>= 2.15.0), moments, VGAM
License: GPL-2