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, 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.
Researchers from the University of Freiburg have developed RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. The researchers 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. They 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), the researchers also present their influence on benchmark set performance. Finally, they show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP.
Overview of the RNAProt framework
The yellow boxes mark necessary framework inputs, the blue boxes mark the 5 program modes of RNAProt, and the green boxes mark the framework outputs. Arrows show the dependencies between inputs, modes, and outputs.
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.
Availability – Project page: https://github.com/BackofenLab/RNAProt