@Article{Uhl_Tran_Heyl-RNAPr_effic_and-2021,
  author =	 {Uhl, Michael and Tran, Van Dinh and Heyl, Florian and 
                  Backofen, Rolf},
  title =	 {{RNAProt}: an efficient and feature-rich {RNA} binding 
                  protein binding site predictor},
  journal =	 {Gigascience},
  year =	 {2021},
  volume =	 {10},
  number =	 {8},
  pages =	 {},
  user =	 {backofen},
  pmid =	 {34406415},
  doi = 	 {10.1093/gigascience/giab054},
  issn = 	 {2047-217X},
  abstract =	 {BACKGROUND: Cross-linking and immunoprecipitation followed 
                  by next-generation sequencing (CLIP-seq) is the 
                  state-of-the-art technique used to experimentally determine 
                  transcriptome-wide binding sites of RNA-binding proteins 
                  (RBPs). However, it relies on gene expression, which can be 
                  highly variable between conditions and thus cannot provide a 
                  complete picture of the RBP binding landscape. This creates 
                  a demand for computational methods to predict missing 
                  binding sites. Although there exist various methods using 
                  traditional machine learning and lately also deep learning, 
                  we encountered several problems: many of these are not well 
                  documented or maintained, making them difficult to install 
                  and use, or are not even available. In addition, there can 
                  be efficiency issues, as well as little flexibility 
                  regarding options or supported features. RESULTS: Here, we 
                  present RNAProt, an efficient and feature-rich computational 
                  RBP binding site prediction framework based on recurrent 
                  neural networks. We compare RNAProt with 1 traditional 
                  machine learning approach and 2 deep-learning methods, 
                  demonstrating its state-of-the-art predictive performance 
                  and better run time efficiency. We further show that its 
                  implemented visualizations capture known binding preferences 
                  and thus can help to understand what is learned. Since 
                  RNAProt supports various additional features (including 
                  user-defined features, which no other tool offers), we also 
                  present their influence on benchmark set performance. 
                  Finally, we show the benefits of incorporating additional 
                  features, specifically structure information, when learning 
                  the binding sites of an hairpin loop binding RBP. 
                  CONCLUSIONS: RNAProt provides a complete framework for RBP 
                  binding site predictions, from data set generation over 
                  model training to the evaluation of binding preferences and 
                  prediction. It offers state-of-the-art predictive 
                  performance, as well as superior run time efficiency, while 
                  at the same time supporting more features and input types 
                  than any other tool available so far. RNAProt is easy to 
                  install and use, comes with comprehensive documentation, and 
                  is accompanied by informative statistics and visualizations. 
                  All this makes RNAProt a valuable tool to apply in future 
                  RBP binding site research.}
}