Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied seven machine learning algorithms (Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Neural Networks and Deep Learning) through model organisms from different evolutionary branches to create a stand-alone and web server tool (RNAmining) to distinguish coding and non-coding sequences. Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their trinucleotides count analysed (64 features) and we performed a normalization by the sequence length, resulting in total of 180 models. The machine learning algorithms validations were performed using 10-fold cross-validation and we selected the algorithm with the best results (eXtreme Gradient Boosting) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and TransDecoder) and our results outperformed them. Both stand-alone and web server versions of RNAmining are freely available at https://rnamining.integrativebioinformatics.me/.

Machine Learning; benchmarking; coding potential prediction; non-coding RNA