LncRNA-ID: Long non-coding RNA IDentification using balanced random forests

Bioinformatics
Rujira AchawanantakunYuan Zhang

Abstract

Long non-coding RNAs (lncRNAs), which are non-coding RNAs of length above 200 nucleotides, play important biological functions such as gene expression regulation. To fully reveal the functions of lncRNAs, a fundamental step is to annotate them in various species. However, as lncRNAs tend to encode one or multiple open reading frames, it is not trivial to distinguish these long non-coding transcripts from protein-coding genes in transcriptomic data. In this work, we design a new tool that calculates the coding potential of a transcript using a machine learning model (random forest) based on multiple features including sequence characteristics of putative open reading frames, translation scores based on ribosomal coverage, and conservation against characterized protein families. The experimental results show that our tool competes favorably with existing coding potential computation tools in lncRNA identification. The scripts and data can be downloaded at https://github.com/zhangy72/LncRNA-ID.

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Citations

Jul 17, 2018·Briefings in Bioinformatics·Seo-Won ChoiJin-Wu Nam
Jan 6, 2017·Nucleic Acids Research·Valentin WucherThomas Derrien
Apr 17, 2018·Non-coding RNA·Lucas Maciel VieiraMaria Emilia M T Walter
Dec 19, 2020·Frontiers in Cell and Developmental Biology·Yunjin LiGeng Chen
Jul 8, 2021·Interdisciplinary Sciences, Computational Life Sciences·Li ZhangHongsheng Liu
Aug 28, 2021·International Journal of Molecular Sciences·Muhammad Nabeel AsimSheraz Ahmed

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