Paper Info
Title
Site identification in high-throughput RNA–protein interaction data
Abstract
Motivation: Post-transcriptional and co-transcriptional regulation is a crucial link between genotype and phenotype. The central players are the RNA-binding proteins, and experimental technologies [such as cross-linking with immunoprecipitation-(CLIP-) and RIP-seq] for probing their activities have advanced rapidly over the course of the past decade. Statistically robust, flexible computational methods for binding site identification from high-throughput immunoprecipitation assays are largely lacking however. Results: We introduce a method for site identification which provides four key advantages over previous methods: (i) it can be applied on all variations of CLIP and RIP-seq technologies, (ii) it accurately models the underlying read-count distributions, (iii) it allows external covariates, such as transcript abundance (which we demonstrate is highly correlated with read count) to inform the site identification process and (iv) it allows for direct comparison of site usage across cell types or conditions. Availability and implementation: We have implemented our method in a software tool called Piranha. Source code and binaries, licensed under the GNU General Public License (version 3) are freely available for download from http://smithlab.usc.edu. Contact: andrewds@usc.edu Supplementary information:Supplementary data available at Bioinformatics online.
Year
DOI
Venue
2012
10.1093/bioinformatics/bts569
Bioinformatics
Keywords
DocType
Volume
rna binding proteins,hek293 cells,binding sites,rna binding protein,rna,computational biology
Journal
28
Issue
ISSN
Citations 
23
1367-4803
15
PageRank 
References 
Authors
1.60
4
10
Name
Order
Citations
PageRank
Philip J. Uren1161.96
Emad Bahrami Samani2151.60
Suzanne C. Burns3151.60
Mei Qiao4202.03
Fedor V. Karginov5151.60
Emily Hodges6516.44
Gregory J Hannon7587.83
Jeremy R. Sanford8151.60
Luiz O. F. Penalva9212.39
Andrew D Smith1043847.50