Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants. (24th April 2018)

• Record Type: Journal Article
• Authors: Negri, Tatianne da Costa; Alves, Wonder Alexandre Luz; Bugatti, Pedro Henrique; Saito, Priscila Tiemi Maeda; Domingues, Douglas Silva; Paschoal, Alexandre Rossi
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 2(2019)
• Page Start: 682
• Page End: 689
• Publication Date: 2018-04-24
• Subjects: bioinformatics -- tool -- features -- machine learning -- long RNAs -- pattern recognition; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby034
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/ ) to identify lncRNAs in plants. Results: Our feature selection analysis considered 5468 features, and it used only 16 features to robustly identify lncRNA with the REPTree algorithm. That was the base to create the model and train it with lncRNA and mRNA data from five plant species (thale cress, cucumber, soybean, poplar and Asian rice). After an extensive comparison with other tools largely used in plants (CPC, CPC2, CPAT and PLncPRO), we found that RNAplonc produced more reliable lncRNA predictions from plant transcripts with 87.5% of the best result in eight tests in eight species from the GreeNC database and four independent studies in monocotyledonous ( Brachypodium ) and eudicotyledonous ( Populus and Gossypium ) species.