IRSOM2: a web server for predicting bifunctional RNAs

Author:

Postic Guillaume1ORCID,Tav Christophe1,Platon Ludovic1,Zehraoui Farida1,Tahi Fariza1

Affiliation:

1. Université Paris-Saclay, Univ Evry, IBISC , 91020 , Evry-Courcouronnes , France

Abstract

Abstract Recent advances have shown that some biologically active non-coding RNAs (ncRNAs) are actually translated into polypeptides that have a physiological function as well. This paradigm shift requires adapted computational methods to predict this new class of ‘bifunctional RNAs’. Previously, we developed IRSOM, an open-source algorithm to classify non-coding and coding RNAs. Here, we use the binary statistical model of IRSOM as a ternary classifier, called IRSOM2, to identify bifunctional RNAs as a rejection of the two other classes. We present its easy-to-use web interface, which allows users to perform predictions on large datasets of RNA sequences in a short time, to re-train the model with their own data, and to visualize and analyze the classification results thanks to the implementation of self-organizing maps (SOM). We also propose a new benchmark of experimentally validated RNAs that play both protein-coding and non-coding roles, in different organisms. Thus, IRSOM2 showed promising performance in detecting these bifunctional transcripts among ncRNAs of different types, such as circRNAs and lncRNAs (in particular those of shorter lengths). The web server is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Funder

Université Paris-Saclay, Univ Evry, IBISC

Publisher

Oxford University Press (OUP)

Subject

Genetics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparison and benchmark of deep learning methods for non-coding RNA classification;PLOS Computational Biology;2024-09-12

2. Deciphering the Complex Characterization of Coding LncRNA;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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