Prelnc2: A prediction tool for lncRNAs with enhanced multi-level features of RNAs

• Published in: PloS one Vol. 18; no. 6; p. e0286377
• Main Authors: Gao, Hua, Gao, Peng, Ye, Ning
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Annotations; Biology and life sciences; Classification; Datasets; Engineering and Technology; Fourier transforms; Gene expression; Genomes; Genomics; Health aspects; Neural networks; Non-coding RNA; Research and Analysis Methods; Research methodology; RNA sequencing; Signal processing; Wavelet transforms; Iran
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0286377

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.