The in-depth study of viral genomes is of great help in many aspects, especially in the treatment of human diseases caused by viral infections. With the rapid accumulation of viral sequencing data, improved, or alternative gene-finding systems have become necessary to process and mine these data. In this article, we present Vgas, a system combining an ab initio method and a similarity-based method to automatically find viral genes and perform gene function annotation. Vgas was compared with existing programs, such as Prodigal, GeneMarkS, and Glimmer. Through testing 5,705 virus genomes downloaded from RefSeq, Vgas demonstrated its superiority with the highest average precision and recall (both indexes were 1% higher or more than the other programs); particularly for small virus genomes (≤ 10 kb), it showed significantly improved performance (precision was 6% higher, and recall was 2% higher). Moreover, Vgas presents an annotation module to provide functional information for predicted genes based on BLASTp alignment. This characteristic may be specifically useful in some cases. When combining Vgas with GeneMarkS and Prodigal, better prediction results could be obtained than with each of the three individual programs, suggesting that collaborative prediction using several different software programs is an alternative for gene prediction. Vgas is freely available at http://cefg.uestc.cn/vgas/ or http://121.48.162.133/vgas/ . We hope that Vgas could be an alternative virus gene finder to annotate new genomes or reannotate existing genome.

Geptop has performed effectively in the identification of prokaryotic essential genes since its first release in 2013. It estimates gene essentiality for prokaryotes based on orthology and phylogeny. Genome-scale essentiality data of more prokaryotic species are available, and the information has been collected into public essential gene repositories such as DEG and OGEE. A faster and more accurate toolkit is needed to meet the increasing prokaryotic genome data. We updated Geptop by supplementing more validated essentiality data into reference set (from 19 to 37 species), and introducing multi-process technology to accelerate the computing speed. Compared with Geptop 1.0 and other gene essentiality prediction models, Geptop 2.0 can generate more stable predictions and finish the computation in a shorter time. The software is available both as an online server and a downloadable standalone application. We hope that the improved Geptop 2.0 will facilitate researches in gene essentiality and the development of novel antibacterial drugs. The gene essentiality prediction tool is available at http://cefg.uestc.cn/geptop .

Bats (order Chiroptera) are one of the most diverse and widely distributed group of mammals with a close relationship to humans. Over the past few decades, a number of studies have been performed on bat viruses; in contrast, bacterial pathogens carried by bats were largely neglected. As more bacterial pathogens are being identified from bats, the need to study their natural microbiota is becoming urgent. In the current study, fecal samples of four bat species from different locations of China were analyzed for their microbiota composition. Together with the results of others, we concluded that bat microbiota is most commonly dominated by Firmicutes and Proteobacteria; the strict anaerobic phylum Bacteroidetes, which is dominant in other terrestrial mammals, especially humans and mice, is relatively rare in bats. This phenomenon was interpreted as a result of a highly specified gastrointestinal tract in adaptation to the flying lifestyle of bats. Further comparative study implied that bat microbiota resemble those of the order Carnivora. To discover potential bacterial pathogens, a database was generated containing the 16S rRNA gene sequences of known bacterial pathogens. Potential bacterial pathogens belonging to 12 genera were detected such as Salmonella, Shigella, and Yersinia, among which some have been previously reported in bats. This study demonstrated high resolution and repeatability in detecting organisms of rare existence, and the results could be used as guidance for future bacterial pathogen isolation.