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A SUPPORT VECTOR MACHINE BASED METHOD TO DISTINGUISH LONG NON-CODING RNAS FROM PROTEIN CODING TRANSCRIPTS
Machine learning
Principal component analysis (PCA)
Support vector machine (SVM)
lncRNA prediction with nucleotide pattern frequencies and ORF length
Camundongos
Anotação de Sequência Molecular
Fases de Leitura Aberta
RNA Mensageiro
RNA não Traduzido
Peixe-Zebra
Máquina de Vetores de Suporte
Author
Affilliation
University of Brasilia. Instituto de Ciências Exatas. Department of Computer Science. Brasília, DF, Brazil.
Fundação Oswaldo Cruz. Fiocruz Brasília. Brasília, DF, Brasil.
University of Brasilia. Instituto de Ciencias Biologicas. Laboratory of Molecular Biology. Brasília, DF, Brazil.
University of Brasilia. Instituto de Ciências Exatas. Department of Computer Science. Brasília, DF, Brazil.
University of Leipzig. Department of Computer Science and Interdisciplinary Center for Bioinformatics. Bioinformatics Group. Leipzig, Germany.
Fundação Oswaldo Cruz. Fiocruz Brasília. Brasília, DF, Brasil.
University of Brasilia. Instituto de Ciencias Biologicas. Laboratory of Molecular Biology. Brasília, DF, Brazil.
University of Brasilia. Instituto de Ciências Exatas. Department of Computer Science. Brasília, DF, Brazil.
University of Leipzig. Department of Computer Science and Interdisciplinary Center for Bioinformatics. Bioinformatics Group. Leipzig, Germany.
Abstract
Background: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of
sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important
problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and
protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish
lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.
Methods: The proposed method is based on SVM and uses the first ORF relative length and frequencies of
nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These
features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA
were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction.
Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to
create the best model to distinguish lncRNAs from PCTs.
Results: This method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish
(Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other
tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an
improvement in accuracy by ≈ 3.00%. In addition, to validate our model, the mouse data was classified with the
human model, and vice-versa, achieving ≈ 97.80% accuracy in both cases, showing that the model is not overfit. The
SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila
melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of
human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was
capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being
lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified
lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully
classified 98.62%, 80.8% and 91.9%, respectively.
Conclusions: The SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs,
as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used
PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs
from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish
lncRNAs of even more distant species.
Keywords
Long non-coding RNA (lncRNA)Machine learning
Principal component analysis (PCA)
Support vector machine (SVM)
lncRNA prediction with nucleotide pattern frequencies and ORF length
DeCS
Biologia ComputacionalCamundongos
Anotação de Sequência Molecular
Fases de Leitura Aberta
RNA Mensageiro
RNA não Traduzido
Peixe-Zebra
Máquina de Vetores de Suporte
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