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RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction

Thaís A. R. Ramos, Nilbson R. O. Galindo, Raúl Arias-Carrasco, Cecília F. da Silva, View ORCID ProfileVinicius Maracaja-Coutinho, Thaís G. do Rêgo
doi: https://doi.org/10.1101/2020.10.26.354357
Thaís A. R. Ramos
1Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Brazil
2Departamento de Informática, Centro de Informática, Universidade Federal da Paraíba, João Pessoa, Brazil
3Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
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Nilbson R. O. Galindo
2Departamento de Informática, Centro de Informática, Universidade Federal da Paraíba, João Pessoa, Brazil
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Raúl Arias-Carrasco
3Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
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Cecília F. da Silva
2Departamento de Informática, Centro de Informática, Universidade Federal da Paraíba, João Pessoa, Brazil
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Vinicius Maracaja-Coutinho
1Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Brazil
3Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
4Instituto Vandique, João Pessoa, Brazil
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  • ORCID record for Vinicius Maracaja-Coutinho
  • For correspondence: gaudenciothais@gmail.com vinicius.maracaja@uchile.cl
Thaís G. do Rêgo
1Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Brazil
2Departamento de Informática, Centro de Informática, Universidade Federal da Paraíba, João Pessoa, Brazil
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  • For correspondence: gaudenciothais@gmail.com vinicius.maracaja@uchile.cl
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ABSTRACT

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 7 machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, ANN and DL) through 15 model organisms from different evolutionary branches. Then, we created a stand-alone and web server tool (RNAmining) to distinguish coding and noncoding sequences, selecting the algorithm with the best performance (XGBoost). Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their tri-nucleotides counts analysed and we performed a normalization by the sequence length. Thus, in total we built 180 models. All the machine learning algorithms tests were performed using 10-folds cross-validation and we selected the algorithm with the best results (XGBoost) 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, opening opportunities for the development of RNAmining, which is freely available at https://rnamining.integrativebioinformatics.me/.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://rnamining.integrativebioinformatics.me/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction
Thaís A. R. Ramos, Nilbson R. O. Galindo, Raúl Arias-Carrasco, Cecília F. da Silva, Vinicius Maracaja-Coutinho, Thaís G. do Rêgo
bioRxiv 2020.10.26.354357; doi: https://doi.org/10.1101/2020.10.26.354357
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RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction
Thaís A. R. Ramos, Nilbson R. O. Galindo, Raúl Arias-Carrasco, Cecília F. da Silva, Vinicius Maracaja-Coutinho, Thaís G. do Rêgo
bioRxiv 2020.10.26.354357; doi: https://doi.org/10.1101/2020.10.26.354357

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