@article{Maticzka_Lange_Costa-Graph_model_bindi-2014,
author = {Maticzka, Daniel and Lange, Sita J. and Costa, Fabrizio and 
          Backofen, Rolf},
title = {{GraphProt}: modeling binding preferences of {RNA}-binding 
         proteins},
journal = {Genome Biol},
year = {2014},
doi = {10.1186/gb-2014-15-1-r17},
volume = {15},
user = {sita},
pmid = {24451197},
pages = {R17},
number = {1},
issn = {1465-6914},
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 to predict RBP-binding sites and 
            affinities in all tissues. GraphProt is freely available at 
            http://www.bioinf.uni-freiburg.de/Software/GraphProt.}
}