@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},
  volume =	 {15},
  number =	 {1},
  pages =	 {R17},
  user =	 {sita},
  pmid =	 {24451197},
  doi = 	 {10.1186/gb-2014-15-1-r17},
  issn = 	 {1465-6914},
  issn = 	 {1465-6906},
  issn =     {1474-7596},
  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.}
}