MiClip: 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 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.

Version: 1.2
Depends: R (≥ 2.15.0), moments, VGAM
Published: 2013-11-16
Author: Tao Wang
Maintainer: Tao Wang <tao.wang at utsouthwestern.edu>
License: GPL-2
NeedsCompilation: no
SystemRequirements: Perl
Materials: NEWS
CRAN checks: MiClip results

Downloads:

Reference manual: MiClip.pdf
Vignettes: MiClip
Package source: MiClip_1.2.tar.gz
OS X binary: MiClip_1.2.tgz
Windows binary: MiClip_1.2.zip
Old sources: MiClip archive