NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae

Chandran Nithin; Sunandan Mukherjee; Jolly Basak; Ranjit Prasad Bahadur

doi:10.1017/qpb.2022.18

NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae

Quant Plant Biol. 2022 Oct 7:3:e23. doi: 10.1017/qpb.2022.18. eCollection 2022.

Authors

Chandran Nithin^{1

2}, Sunandan Mukherjee^{1

3}, Jolly Basak⁴, Ranjit Prasad Bahadur¹

Affiliations

¹ Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India.
² Laboratory of Computational Biology, Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-089 Warsaw, Poland.
³ Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, PL-02-109 Warsaw, Poland.
⁴ Department of Biotechnology, Visva-Bharati, Santiniketan, 731235, India.

Abstract

Non-coding RNAs (ncRNAs) are major players in the regulation of gene expression. This study analyses seven classes of ncRNAs in plants using sequence and secondary structure-based RNA folding measures. We observe distinct regions in the distribution of AU content along with overlapping regions for different ncRNA classes. Additionally, we find similar averages for minimum folding energy index across various ncRNAs classes except for pre-miRNAs and lncRNAs. Various RNA folding measures show similar trends among the different ncRNA classes except for pre-miRNAs and lncRNAs. We observe different k-mer repeat signatures of length three among various ncRNA classes. However, in pre-miRs and lncRNAs, a diffuse pattern of k-mers is observed. Using these attributes, we train eight different classifiers to discriminate various ncRNA classes in plants. Support vector machines employing radial basis function show the highest accuracy (average F1 of ~96%) in discriminating ncRNAs, and the classifier is implemented as a web server, NCodR.

Keywords: RNA folding; SVM classifier; k-mer repeats; ncRNA prediction; non-coding RNA.