A taxonomic description of bacteria was deduced from 5.78 Mb metagenomic sequence retrieved from Tulsi Shyam hot spring, India using bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). Metagenome contained 10,893 16S rDNA sequences that were analyzed by MG-RAST server to generate the comprehensive profile of bacteria. Metagenomic data are available at EBI under EBI Metagenomics database with accession no. ERP009559. Metagenome sequences represented the 98.2% bacteria origin, 1.5% of eukaryotic and 0.3% were unidentified. A total of 16 bacterial phyla demonstrating 97 families and 287 species were revealed in the hot spring metagenome. Most abundant phyla were Firmicutes (65.38%), Proteobacteria (21.21%) and unclassified bacteria (10.69%). Whereas, Peptostreptococcaceae (37.33%), Clostridiaceae (23.36%), and Enterobacteriaceae (16.37%) were highest reported families in metagenome. Ubiquitous species were Clostridium bifermentans (17.47%), Clostridium lituseburense (13.93%) and uncultured bacterium (10.15%). Our data provide new information on hot spring bacteria and shed light on their abundance, diversity, distribution and coexisting organisms.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

Here, we report metagenome from the Tuwa hot spring, India using shotgun sequencing approach. Metagenome consisted of 541,379 sequences with 98.7 Mbps size with 46% G + C content. Metagenomic sequence reads were deposited into the EMBL database under accession number ERP009321. Community analysis presented 99.1% sequences belong to bacteria, 0.3% of eukaryotic origin, 0.2% virus derived and 0.05% from archea. Unclassified and unidentified sequences were 0.4% and 0.07% respectively. A total of 22 bacterial phyla include 90 families and 201 species were observed in the hot spring metagenome. Firmicutes (97.0%), Proteobacteria (1.3%) and Actinobacteria (0.4%) were reported as dominant bacterial phyla. In functional analysis using Cluster of Orthologous Group (COG), 21.5% drops in the poorly characterized group. Using subsystem based annotation, 4.0% genes were assigned for stress responses and 3% genes were fit into the metabolism of aromatic compounds. The hot spring metagenome is very rich with novel sequences affiliated to unclassified and unidentified lineages, suggesting the potential source for novel microbial species and their products.

The development of next-generation sequencing (NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.