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

作者：Guangyu Wang, Hongyan Yin, Boyang Li, Chunlei Yu, Fan Wang, Xingjian Xu, Jiabao Cao, Yīmíng Bào, Liguo Wang, Amir Ali Abbasi, Vladimir B. Bajić, Lina Ma, Zhang Zhang · 发表于：Bioinformatics · 年份：2019 · DOI：10.1093/bioinformatics/btz008 · 被引用次数：162 · 研究领域：Cancer-related molecular mechanisms research、Genomics and Phylogenetic Studies、Machine Learning in Bioinformatics

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 in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (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 ...