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Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.

Tatianne da Costa Negri¹, Wonder Alexandre Luz Alves², Pedro Henrique Bugatti³

¹Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil.

Briefings in Bioinformatics

|April 27, 2018

Summary

This summary is machine-generated.

Researchers developed RNAplonc, a machine learning tool to accurately identify long noncoding RNAs (lncRNAs) in plants. This computational approach addresses the lack of specific tools for plant lncRNA prediction, improving gene expression regulation studies.

Keywords:

bioinformatics features long RNAs machine learning pattern recognition tool

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Area of Science:

Plant molecular biology
Bioinformatics
Computational biology

Background:

Long noncoding RNAs (lncRNAs) are crucial regulators of gene expression in eukaryotes.
Existing computational tools for lncRNA prediction are primarily designed for mammals, with a notable gap for plant species.
Distinct biological mechanisms in plants necessitate specialized prediction approaches.

Purpose of the Study:

To develop and present a novel computational approach, RNAplonc, for reliable lncRNA identification in plants.
To address the limitations of current prediction tools in the context of plant biology.

Main Methods:

Utilized a machine learning classifier (REPTree algorithm) for lncRNA prediction.
Performed feature selection, identifying 16 key features from an initial set of 5468.
Trained the model using lncRNA and mRNA data from five diverse plant species.

Main Results:

RNAplonc demonstrated robust lncRNA identification capabilities in plants.
Achieved 87.5% accuracy in extensive comparisons against established plant lncRNA prediction tools (CPC, CPC2, CPAT, PLncPRO).
Validated performance across multiple plant species, including monocots and eudicots, using independent datasets.

Conclusions:

RNAplonc offers a reliable and accurate method for predicting lncRNAs in plants.
The tool enhances the study of gene regulation by noncoding RNAs in diverse plant species.
RNAplonc represents a significant advancement in plant bioinformatics tools.