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

Thaís A R Ramos1,2,3, Nilbson R O Galindo2, Raúl Arias-Carrasco3

  • 1Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil.

F1000Research
|June 25, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed RNAmining, a tool to distinguish coding and non-coding RNA sequences using machine learning. This method accurately identifies RNA types across various organisms, outperforming existing tools.

Keywords:
Machine Learningbenchmarkingcoding potential predictionnon-coding RNA

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in cellular regulation across diverse organisms.
  • Accurately distinguishing coding from non-coding RNA sequences is fundamental for ncRNA research.

Purpose of the Study:

  • To develop a robust computational tool for differentiating coding and non-coding RNA sequences.
  • To create a user-friendly, standalone, and web server-based application for sequence classification.

Main Methods:

  • Applied seven machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, Neural Networks, Deep Learning) to model organisms.
  • Utilized trinucleotide counts (64 features) and sequence length normalization, generating 180 models.
  • Validated models using 10-fold cross-validation, selecting eXtreme Gradient Boosting for the final tool.

Main Results:

  • Achieved high F1-scores ranging from 97.56% to 99.57% across different organisms.
  • RNAmining demonstrated superior performance compared to existing tools like CPAT, CPC2, RNAcon, and TransDecoder in benchmarking tests.
  • Developed both standalone and web server versions of the RNAmining tool.

Conclusions:

  • RNAmining provides a highly accurate and efficient method for classifying coding and non-coding RNA sequences.
  • The tool's performance surpasses current state-of-the-art methods, offering a valuable resource for the research community.
  • RNAmining is freely accessible, promoting broader adoption and advancement in ncRNA research.