SOrt-ITEMS software is available for download from: http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS. No license is needed for academic and nonprofit use.

Dynamics of Vaginal-Microbiome and Gonadal-Hormones suggest that elevated vaginal microbial diversity in pregnancy does not necessarily indicate a state of bacterial infection. The study puts forward a hormone-level driven microbiome diversity hypothesis for explaining temporal patterns in vaginal microbial diversity during various stages of women's reproductive cycle and at menopause.

Fructo-oligosaccharides (FOS), a prebiotic supplement, is known for its Bifidogenic capabilities. However, aspects such as effect of variable quantities of FOS intake on gut microbiota, and temporal dynamics of gut microbiota (transitioning through basal, dosage, and follow-up phases) has not been studied in detail. This study investigated these aspects through a randomized, double-blind, placebo-controlled, dose-response relationship study. The study involved 80 participants being administered FOS at three dose levels (2.5, 5, and 10 g/day) or placebo (Maltodextrin 10 g/day) during dosage phase. Microbial DNA extracted from fecal samples collected at 9 intervening time-points was sequenced and analysed. Results indicate that FOS consumption increased the relative abundance of OTUs belonging to Bifidobacterium and Lactobacillus . Interestingly, higher FOS dosage appears to promote, in contrast to Maltodextrin, the selective proliferation of OTUs belonging to Lactobacillus . While consumption of prebiotics increased bacterial diversity, withdrawal led to its reduction. Apart from probiotic bacteria, a significant change was also observed in certain butyrate-producing microbes like Faecalibacterium , Ruminococcus and Oscillospira . The positive impact of FOS on butyrate-producing bacteria and FOS-mediated increased bacterial diversity reinforces the role of prebiotics in conferring beneficial functions to the host.

BackgroundThe overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak—a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.ResultsValidation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.ConclusionsWith novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.Availability and ImplementationA web implementation of Vikodak can be publicly accessed at: http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users (academic as well as commercial).

BackgroundRecent advances in sequencing technologies have resulted in an unprecedented increase in the number of metagenomes that are being sequenced world-wide. Given their volume, functional annotation of metagenomic sequence datasets requires specialized computational tools/techniques. In spite of having high accuracy, existing stand-alone functional annotation tools necessitate end-users to perform compute-intensive homology searches of metagenomic datasets against "multiple" databases prior to functional analysis. Although, web-based functional annotation servers address to some extent the problem of availability of compute resources, uploading and analyzing huge volumes of sequence data on a shared public web-service has its own set of limitations. In this study, we present COGNIZER, a comprehensive stand-alone annotation framework which enables end-users to functionally annotate sequences constituting metagenomic datasets. The COGNIZER framework provides multiple workflow options. A subset of these options employs a novel directed-search strategy which helps in reducing the overall compute requirements for end-users. The COGNIZER framework includes a cross-mapping database that enables end-users to simultaneously derive/infer KEGG, Pfam, GO, and SEED subsystem information from the COG annotations.ResultsValidation experiments performed with real-world metagenomes and metatranscriptomes, generated using diverse sequencing technologies, indicate that the novel directed-search strategy employed in COGNIZER helps in reducing the compute requirements without significant loss in annotation accuracy. A comparison of COGNIZER's results with pre-computed benchmark values indicate the reliability of the cross-mapping database employed in COGNIZER.ConclusionThe COGNIZER framework is capable of comprehensively annotating any metagenomic or metatranscriptomic dataset from varied sequencing platforms in functional terms. Multiple search options in COGNIZER provide end-users the flexibility of choosing a homology search protocol based on available compute resources. The cross-mapping database in COGNIZER is of high utility since it enables end-users to directly infer/derive KEGG, Pfam, GO, and SEED subsystem annotations from COG categorizations. Furthermore, availability of COGNIZER as a stand-alone scalable implementation is expected to make it a valuable annotation tool in the field of metagenomic research.Availability and ImplementationA Linux implementation of COGNIZER is freely available for download from the following links: http://metagenomics.atc.tcs.com/cognizer, https://metagenomics.atc.tcs.com/function/cognizer.