Journal
BRIEFINGS IN BIOINFORMATICS
Volume 20, Issue 2, Pages 682-689Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bby034
Keywords
bioinformatics; tool; features; machine learning; long RNAs; pattern recognition
Funding
- Fundacao Araucaria [019/2015]
- National Council of Technological and Scientific Development (CNPq) of Brazil [00454505/2014-0, 431668/2016-7, 422811/2016-5]
- CNPq research fellowship [309642/2015-9]
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available