GraphProt: Modeling Binding Preferences of RNA-binding Proteins

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2014 Jan 24

PMID 24451197

Citations 125

Authors

Daniel Maticzka

Sita J Lange

Fabrizio Costa

Rolf Backofen

Affiliations

Soon will be listed here.

Abstract

We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt.

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