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

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.

The 1972 Meadows report: A wake-up call for plant science

“…In 2022, Quantitative Plant Biology has showcased new questions in plant science, such as solid versus liquid signalling (van Schijndel et al, 2022 ) or the new role of threonine in skotomorphogenesis (Tabeta et al, 2022 ). New quantitative tools were introduced, from non-coding long RNAs identification and classification (Nithin et al, 2022 ) up to ecosystem natural capital accounting in local territories (Argüello et al, 2022 ). Several articles took a step back on plant science, with new evolutionary views, for instance, on shoot apical meristems (Wu et al, 2022 ), while others explored its future, notably with the rise of transdisciplinary approaches such as citizen science (Receveur et al, 2022 ) and art & science (Bonneval, 2022 ).…”