Recent advances in ultra-high-throughput sequencing technology and metagenomics have led to a paradigm shift in microbial genomics from few genome comparisons to large-scale pan-genome studies at different scales of phylogenetic resolution. Pan-genome studies provide a framework for estimating the genomic diversity of the dataset, determining core (conserved), accessory (dispensable) and unique (strain-specific) gene pool of a species, tracing horizontal gene-flux across strains and providing insight into species evolution. The existing pan genome software tools suffer from various limitations like limited datasets, difficult installation/requirements, inadequate functional features etc. Here we present an ultra-fast computational pipeline BPGA (Bacterial Pan Genome Analysis tool) with seven functional modules. In addition to the routine pan genome analyses, BPGA introduces a number of novel features for downstream analyses like core/pan/MLST (Multi Locus Sequence Typing) phylogeny, exclusive presence/absence of genes in specific strains, subset analysis, atypical G + C content analysis and KEGG & COG mapping of core, accessory and unique genes. Other notable features include minimum running prerequisites, freedom to select the gene clustering method, ultra-fast execution, user friendly command line interface and high-quality graphics outputs. The performance of BPGA has been evaluated using a dataset of complete genome sequences of 28 Streptococcus pyogenes strains.

One of the fundamental issues in the microbiome research is characterization of the healthy human microbiota. Recent studies have elucidated substantial divergences in the microbiome structure between healthy individuals from different race and ethnicity. This review provides a comprehensive account of such geography, ethnicity or life-style-specific variations in healthy microbiome at five major body habitats—Gut, Oral-cavity, Respiratory Tract, Skin, and Urogenital Tract (UGT). The review focuses on the general trend in the human microbiome evolution—a gradual transition in the gross compositional structure along with a continual decrease in diversity of the microbiome, especially of the gut microbiome, as the human populations passed through three stages of subsistence like foraging, rural farming and industrialized urban western life. In general, gut microbiome of the hunter-gatherer populations is highly abundant with Prevotella, Proteobacteria, Spirochaetes, Clostridiales, Ruminobacter etc., while those of the urban communities are often enriched in Bacteroides, Bifidobacterium, and Firmicutes. The oral and skin microbiome are the next most diverse among different populations, while respiratory tract and UGT microbiome show lesser variations. Higher microbiome diversity is observed for oral-cavity in hunter-gatherer group with higher prevalence of Haemophilus than agricultural group. In case of skin microbiome, rural and urban Chinese populations show variation in abundance of Trabulsiella and Propionibacterium. On the basis of published data, we have characterized the core microbiota—the set of genera commonly found in all populations, irrespective of their geographic locations, ethnicity or mode of subsistence. We have also identified the major factors responsible for geography-based alterations in microbiota; though it is not yet clear which factor plays a dominant role in shaping the microbiome—nature or nurture, host genetics or his environment. Some of the geographical/racial variations in microbiome structure have been attributed to differences in host genetics and innate/adaptive immunity, while in many other cases, cultural/behavioral features like diet, hygiene, parasitic load, environmental exposure etc. overshadow genetics. The ethnicity or population-specific variations in human microbiome composition, as reviewed in this report, question the universality of the microbiome-based therapeutic strategies and recommend for geographically tailored community-scale approaches to microbiome engineering.

Providing insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independent of the clinical diagnosis. GMHI is formulated upon 50 microbial species associated with healthy gut ecosystems. These species are identified through a multi-study, integrative analysis on 4347 human stool metagenomes from 34 published studies across healthy and 12 different nonhealthy conditions, i.e., disease or abnormal bodyweight. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of disease presence (or absence) compared to α-diversity indices. Validation on 679 samples from 9 additional studies results in a balanced accuracy of 73.7% in distinguishing healthy from non-healthy groups. Our findings suggest that gut taxonomic signatures can predict health status, and highlight how data sharing efforts can provide broadly applicable discoveries.

BackgroundThe community composition of the human microbiome is known to vary at distinct anatomical niches. But little is known about the nature of variations, if any, at the genome/sub-genome levels of a specific microbial community across different niches. The present report aims to explore, as a case study, the variations in gene repertoire of 28 Prevotella reference genomes derived from different body-sites of human, as reported earlier by the Human Microbiome Consortium.ResultsThe pan-genome for Prevotella remains “open”. On an average, 17% of predicted protein-coding genes of any particular Prevotella genome represent the conserved core genes, while the remaining 83% contribute to the flexible and singletons. The study reveals exclusive presence of 11798, 3673, 3348 and 934 gene families and exclusive absence of 17, 221, 115 and 645 gene families in Prevotella genomes derived from human oral cavity, gastro-intestinal tracts (GIT), urogenital tract (UGT) and skin, respectively. Distribution of various functional COG categories differs significantly among the habitat-specific genes. No niche-specific variations could be observed in distribution of KEGG pathways.ConclusionsPrevotella genomes derived from different body sites differ appreciably in gene repertoire, suggesting that these microbiome components might have developed distinct genetic strategies for niche adaptation within the host. Each individual microbe might also have a component of its own genetic machinery for host adaptation, as appeared from the huge number of singletons.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1350-6) contains supplementary material, which is available to authorized users.