Nithin Chandran, Mukherjee Sunandan, Basak Jolly, Bahadur Ranjit Prasad
Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India.
Laboratory of Computational Biology, Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-089 Warsaw, Poland.
Quant Plant Biol. 2022 Oct 7;3:e23. doi: 10.1017/qpb.2022.18. eCollection 2022.
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.
非编码RNA（ncRNAs）是基因表达调控中的主要参与者。本研究使用基于序列和二级结构的RNA折叠方法分析了植物中的七类ncRNAs。我们观察到不同ncRNA类别的AU含量分布存在不同区域以及重叠区域。此外，除了前体miRNA和长链非编码RNA（lncRNAs）外，我们发现不同ncRNA类别的最小折叠能量指数平均值相似。除了前体miRNA和lncRNAs外，各种RNA折叠方法在不同ncRNA类别中显示出相似的趋势。我们观察到不同ncRNA类别中长度为三的k-mer重复特征不同。然而，在前体miRNA和lncRNAs中，观察到k-mer的分散模式。利用这些属性，我们训练了八个不同的分类器来区分植物中的各种ncRNA类别。采用径向基函数的支持向量机在区分ncRNAs方面显示出最高的准确率（平均F1约为96%），并且该分类器被实现为一个网络服务器，即NCodR。
