Average nucleotide identity (ANI) is a category of computational analysis that can be used to define species boundaries of Archaea and Bacteria. Calculating ANI usually involves the fragmentation of genome sequences, followed by nucleotide sequence search, alignment, and identity calculation. The original algorithm to calculate ANI used the BLAST program as its search engine. An improved ANI algorithm, called OrthoANI, was developed to accommodate the concept of orthology. Here, we compared four algorithms to compute ANI, namely ANIb (ANI algorithm using BLAST), ANIm (ANI using MUMmer), OrthoANIb (OrthoANI using BLAST) and OrthoANIu (OrthoANI using USEARCH) using >100,000 pairs of genomes with various genome sizes. By comparing values to the ANIb that is considered a standard, OrthoANIb and OrthoANIu exhibited good correlation in the whole range of ANI values. ANIm showed poor correlation for ANI of <90%. ANIm and OrthoANIu runs faster than ANIb by an order of magnitude. When genomes that are larger than 7 Mbp were analysed, the run-times of ANIm and OrthoANIu were shorter than that of ANIb by 53- and 22-fold, respectively. In conclusion, ANI calculation can be greatly sped up by the OrthoANIu method without losing accuracy. A web-service that can be used to calculate OrthoANIu between a pair of genome sequences is available at http://www.ezbiocloud.net/tools/ani . For large-scale calculation and integration in bioinformatics pipelines, a standalone JAVA program is available for download at http://www.ezbiocloud.net/tools/orthoaniu .

Genome-based phylogeny plays a central role in the future taxonomy and phylogenetics of Bacteria and Archaea by replacing 16S rRNA gene phylogeny. The concatenated core gene alignments are frequently used for such a purpose. The bacterial core genes are defined as single-copy, homologous genes that are present in most of the known bacterial species. There have been several studies describing such a gene set, but the number of species considered was rather small. Here we present the up-to-date bacterial core gene set, named UBCG, and software suites to accommodate necessary steps to generate and evaluate phylogenetic trees. The method was successfully used to infer phylogenomic relationship of Escherichia and related taxa and can be used for the set of genomes at any taxonomic ranks of Bacteria. The UBCG pipeline and file viewer are freely available at https://www.ezbiocloud.net/tools/ubcg and https://www.ezbiocloud.net/tools/ubcg_viewer , respectively.

Thanks to the recent advancement of DNA sequencing technology, the cost and time of prokaryotic genome sequencing have been dramatically decreased. It has repeatedly been reported that genome sequencing using high-throughput next-generation sequencing is prone to contaminations due to its high depth of sequencing coverage. Although a few bioinformatics tools are available to detect potential contaminations, these have inherited limitations as they only use protein-coding genes. Here we introduce a new algorithm, called ContEst16S, to detect potential contaminations using 16S rRNA genes from genome assemblies. We screened 69 745 prokaryotic genomes from the NCBI Assembly Database using ContEst16S and found that 594 were contaminated by bacteria, human and plants. Of the predicted contaminated genomes, 8 % were not predicted by the existing protein-coding gene-based tool, implying that both methods can be complementary in the detection of contaminations. A web-based service of the algorithm is available at www.ezbiocloud.net/tools/contest16s.

BackgroundRecurrent aphthous stomatitis (RAS) is a common oral mucosal disorder of unclear etiopathogenesis. Although recent studies of the oral microbiota by high-throughput sequencing of 16S rRNA genes have suggested that imbalances in the oral microbiota may contribute to the etiopathogenesis of RAS, no specific bacterial species associated with RAS have been identified. The present study aimed to characterize the microbiota in the oral mucosa and saliva of RAS patients in comparison with control subjects at the species level.ResultsThe bacterial communities of the oral mucosa and saliva from RAS patients with active lesions (RAS, n = 18 for mucosa and n = 8 for saliva) and control subjects (n = 18 for mucosa and n = 7 for saliva) were analyzed by pyrosequencing of the 16S rRNA genes. There were no significant differences in the alpha diversity between the controls and the RAS, but the mucosal microbiota of the RAS patients showed increased inter-subject variability. A comparison of the relative abundance of each taxon revealed decreases in the members of healthy core microbiota but increases of rare species in the mucosal and salivary microbiota of RAS patients. Particularly, decreased Streptococcus salivarius and increased Acinetobacter johnsonii in the mucosa were associated with RAS risk. A dysbiosis index, which was developed using the relative abundance of A. johnsonii and S. salivarius and the regression coefficients, correctly predicted 83 % of the total cases for the absence or presence of RAS. Interestingly, A. johnsonii substantially inhibited the proliferation of gingival epithelial cells and showed greater cytotoxicity against the gingival epithelial cells than S. salivarius.ConclusionRAS is associated with dysbiosis of the mucosal and salivary microbiota, and two species associated with RAS have been identified. This knowledge may provide a diagnostic tool and new targets for therapeutics for RAS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0673-z) contains supplementary material, which is available to authorized users.

With increasing attention being paid to improving emotional well-being, recent evidence points to gut microbiota as a key player in regulating mental and physical health via bidirectional communication between the brain and gut. Here, we examine the association between emotional well-being and gut microbiome profiles (i.e., gut microbiome composition, diversity, and the moderating role of the enterotypes) among healthy Korean adults (n = 83, mean age = 48.9, SD = 13.2). The research was performed using high-throughput 16S rRNA gene sequencing to obtain gut microbiome profiles, as well as a self-report survey that included the Positive Affect Negative Affect Schedule (PANAS). The cluster-based analysis identified two enterotypes dominated by the genera Bacteroides (n = 49) and Prevotella (n = 34). Generalized linear regression analysis reveals significant associations between positive emotion and gut microbiome diversity (Shannon Index) among participants in the Prevotella dominant group, whereas no such relationship emerged among participants in the Bacteroides group. Moreover, a novel genus from the family Lachnospiraceae is associated with emotional well-being scores, both positive and negative. Together, the current findings highlight the enterotype-specific links between the gut microbiota community and emotion in healthy adults and suggest the possible roles of the gut microbiome in promoting mental health.