Tatianne da Costa Negri1, Wonder Alexandre Luz Alves2, Pedro Henrique Bugatti3, Priscila Tiemi Maeda Saito3, Douglas Silva Domingues4, Alexandre Rossi Paschoal3. 1. Department 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. 2. Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil. 3. Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil. 4. Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio, Procópio, Brazil and Department of Botany, Institute of Biosciences, São Paulo State University, UNESP, Rio Claro, SP, Brazil.
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.
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.
Authors: Flávia C de Paula Freitas; Anete P Lourenço; Francis M F Nunes; Alexandre R Paschoal; Fabiano C P Abreu; Fábio O Barbin; Luana Bataglia; Carlos A M Cardoso-Júnior; Mário S Cervoni; Saura R Silva; Fernanda Dalarmi; Marco A Del Lama; Thiago S Depintor; Kátia M Ferreira; Paula S Gória; Michael C Jaskot; Denyse C Lago; Danielle Luna-Lucena; Livia M Moda; Leonardo Nascimento; Matheus Pedrino; Franciene Rabiço Oliveira; Fernanda C Sanches; Douglas E Santos; Carolina G Santos; Joseana Vieira; Angel R Barchuk; Klaus Hartfelder; Zilá L P Simões; Márcia M G Bitondi; Daniel G Pinheiro Journal: BMC Genomics Date: 2020-06-03 Impact factor: 3.969