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

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

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 d...

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Bibliographic Details
Main Authors: Chandran Nithin, Sunandan Mukherjee, Jolly Basak, Ranjit Prasad Bahadur
Format: Article
Language:English
Published: Cambridge University Press 2022-01-01
Series:Quantitative Plant Biology
Subjects:
k-mer repeats
ncRNA prediction
non-coding RNA
RNA folding
SVM classifier
Online Access:https://www.cambridge.org/core/product/identifier/S2632882822000182/type/journal_article
Description
Summary: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.
ISSN:2632-8828

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