Bào, Yīmíng

Abstract 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 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 and implementation LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. Supplementary information Supplementary data are available at Bioinformatics online.

The mononegaviral family Filoviridae has eight members assigned to three genera and seven species. Until now, genus and species demarcation were based on arbitrarily chosen filovirus genome sequence divergence values (≈50% for genera, ≈30% for species) and arbitrarily chosen phenotypic virus or virion characteristics. Here we report filovirus genome sequence-based taxon demarcation criteria using the publicly accessible PAirwise Sequencing Comparison (PASC) tool of the US National Center for Biotechnology Information (Bethesda, MD, USA). Comparison of all available filovirus genomes in GenBank using PASC revealed optimal genus demarcation at the 55–58% sequence diversity threshold range for genera and at the 23–36% sequence diversity threshold range for species. Because these thresholds do not change the current official filovirus classification, these values are now implemented as filovirus taxon demarcation criteria that may solely be used for filovirus classification in case additional data are absent. A near-complete, coding-complete, or complete filovirus genome sequence will now be required to allow official classification of any novel “filovirus.” Classification of filoviruses into existing taxa or determining the need for novel taxa is now straightforward and could even become automated using a presented algorithm/flowchart rooted in RefSeq (type) sequences.