Email updates

Keep up to date with the latest news and content from Genome Biology and BioMed Central.

This article is part of a special issue on RBPome.

Open Access Highly Accessed Open Badges Method

GraphProt: modeling binding preferences of RNA-binding proteins

Daniel Maticzka1, Sita J Lange1, Fabrizio Costa1 and Rolf Backofen12*

Author Affiliations

1 Department of Computer Science, Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany

2 Centre for Biological Signalling Studies (BIOSS), Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany

For all author emails, please log on.

Genome Biology 2014, 15:R17  doi:10.1186/gb-2014-15-1-r17

Published: 22 January 2014


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 webcite.