RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures.

MOTIVATION Applied research in machine learning progresses faster when a clean dataset is available and ready to use. Several datasets have been proposed and released over the years for specific tasks such as image classification, speech-recognition, and more recently for protein structure prediction. However, for the fundamental problem of RNA structure prediction, information is spread between several databases depending on the level we are interested in: sequence, secondary structure, 3 D structure, or interactions with other macromolecules. In order to speed-up advances in machine-learning based approaches for RNA secondary and/or 3 D structure prediction, a dataset integrating all this information is required, to avoid spending time on data gathering and cleaning. RESULTS Here we propose the first attempt of a standardized and automatically generated dataset dedicated to RNA combining together: RNA sequences, homology information (under the form of position-specific scoring matrices), and information derived by annotation of available 3 D structures (including secondary structure, canonical and non-canonical interactions, and backbone torsion angles). The data is retrieved from public databases PDB, Rfam and SILVA. The paper describes the procedure to build such dataset and the RNA structure descriptors we provide. Some statistical descriptions of the resulting dataset are also provided. AVAILABILITY The dataset is updated every month and available online (in flat-text file format) on the EvryRNA software platform (https://evryrna.ibisc.univ-evry.fr/evryrna/rnanet). An efficient parallel pipeline to build the dataset is also provided for easy reproduction or modification. CONTACT louis.becquey@univ-evry.fr, fariza.tahi@univ-evry.fr.