A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts

Peter F. Stadler,Taina Raiol,Maria Emilia M. T. Walter,Hugo W. Schneider,Marcelo M. Brigido

doi:10.1186/s12864-017-4178-4

Peter F. Stadler, Taina Raiol + Show 3 more

Open Access

https://doi.org/10.1186/s12864-017-4178-4

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Abstract

BackgroundIn 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.MethodsThe 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.ResultsThis 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.ConclusionsThe 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.

Highlights

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
We present a Support Vector Machine (SVM) based method to distinguish Long non-coding RNA (lncRNA) from Protein coding transcript (PCT), using features extracted from transcript sequences: frequencies of nucleotide patterns selected by Principal Component Analysis (PCA) [40]; open reading frame (ORF) length; and ORF relative length
These transcript FASTA files contain PCT and lncRNA sequences, while the classification was extracted from the transcript biotype, provided by Ensembl

Summary

Introduction

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. Thousands of sequencing projects around the world have been creating enormous volumes of RNA data, which has led to the discovery and description of a rapidly increasing number of non-coding RNAs (ncRNAs) in eukaryotic genomes [1,2,3,4]. They appear to be involved in virtually all the regulatory processes in the cell They are often pragmatically defined as transcripts of a more than 200 nucleotides in length, and without any apparent coding capacity, lncRNAs are still rather poorly understood [11,12,13]. An extensive literature links lncRNAs with a wide array of diseases [18,19,20,21], the molecular mechanisms underlying lncRNA action are still largely unknown

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Conclusion