Characterization and identification of long non-coding RNAs based on feature relationship

Guangyu Wang, Hongyan Yin, Boyang Li, Chunlei Yu, Fan Wang, Xingjian Xu, Jiabao Cao, Yiming Bao, Liguo Wang, Amir A Abbasi, Vladimir B. Bajic, Lina Ma, Zhang Zhang

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Motivation:The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Results:Here we first characterize lncRNAs by contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between ORF (open reading frame) length and GC content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. Availability:LGC web server is publicly available at The scripts and data can be downloaded at Supplementary information:Supplementary data are available at Bioinformatics online.
Original languageEnglish (US)
Pages (from-to)2949-2956
Number of pages8
Issue number17
StatePublished - Jan 12 2019


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