i-rDNA: alignment-free algorithm for rapid in silico detection of ribosomal gene fragments from metagenomic sequence data sets

<p>Abstract</p> <p>Background</p> <p>Obtaining accurate estimates of microbial diversity using rDNA profiling is the first step in most metagenomics projects. Consequently, most metagenomic projects spend considerable amounts of time, money and manpower for experimentally cloning, amplifying and sequencing the rDNA content in a metagenomic sample. In the second step, the entire genomic content of the metagenome is extracted, sequenced and analyzed. Since DNA sequences obtained in this second step also contain rDNA fragments, rapid <it>in silico</it> identification of these rDNA fragments would drastically reduce the cost, time and effort of current metagenomic projects by entirely bypassing the experimental steps of primer based rDNA amplification, cloning and sequencing. In this study, we present an algorithm called i-rDNA that can facilitate the rapid detection of 16S rDNA fragments from amongst millions of sequences in metagenomic data sets with high detection sensitivity.</p> <p>Results</p> <p>Performance evaluation with data sets/database variants simulating typical metagenomic scenarios indicates the significantly high detection sensitivity of i-rDNA. Moreover, i-rDNA can process a million sequences in less than an hour on a simple desktop with modest hardware specifications.</p> <p>Conclusions</p> <p>In addition to the speed of execution, high sensitivity and low false positive rate, the utility of the algorithmic approach discussed in this paper is immense given that it would help in bypassing the entire experimental step of primer-based rDNA amplification, cloning and sequencing. Application of this algorithmic approach would thus drastically reduce the cost, time and human efforts invested in all metagenomic projects.</p> <p>Availability</p> <p>A web-server for the i-rDNA algorithm is available at <url>http://metagenomics.atc.tcs.com/i-rDNA/</url></p

HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences

<p>Abstract</p> <p>Background</p> <p>One of the primary goals of comparative metagenomic projects is to study the differences in the microbial communities residing in diverse environments. Besides providing valuable insights into the inherent structure of the microbial populations, these studies have potential applications in several important areas of medical research like disease diagnostics, detection of pathogenic contamination and identification of hitherto unknown pathogens. Here we present a novel and rapid, alignment-free method called HabiSign, which utilizes patterns of tetra-nucleotide usage in microbial genomes to bring out the differences in the composition of both diverse and related microbial communities.</p> <p>Results</p> <p>Validation results show that the metagenomic signatures obtained using the HabiSign method are able to accurately cluster metagenomes at biome, phenotypic and species levels, as compared to an average tetranucleotide frequency based approach and the recently published dinucleotide relative abundance based approach. More importantly, the method is able to identify subsets of sequences that are specific to a particular habitat. Apart from this, being alignment-free, the method can rapidly compare and group multiple metagenomic data sets in a short span of time.</p> <p>Conclusions</p> <p>The proposed method is expected to have immense applicability in diverse areas of metagenomic research ranging from disease diagnostics and pathogen detection to bio-prospecting. A web-server for the HabiSign algorithm is available at <url>http://metagenomics.atc.tcs.com/HabiSign/</url>.</p

iVikodak—A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities

Background: The objectives of any metagenomic study typically include identification of resident microbes and their relative proportions (taxonomic analysis), profiling functional diversity (functional analysis), and comparing the identified microbes and functions with available metadata (comparative metagenomics). Given the advantage of cost-effectiveness and convenient data-size, amplicon-based sequencing has remained the technology of choice for exploring phylogenetic diversity of an environment. A recent school of thought, employing the existing genome annotation information for inferring functional capacity of an identified microbiome community, has given a promising alternative to Whole Genome Shotgun sequencing for functional analysis. Although a handful of tools are currently available for function inference, their scope, functionality and utility has essentially remained limited. Need for a comprehensive framework that expands upon the existing scope and enables a standardized workflow for function inference, analysis, and visualization, is therefore felt.Methods: We present iVikodak, a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface. iVikodak is equipped with microbial co-inhabitance pattern driven published algorithms along with multiple updated databases of various curated microbe-function maps. It also features an advanced task management and result sharing system through introduction of personalized and portable dashboards.Results: In addition to inferring functions from 16S rRNA gene data, iVikodak enables (a) an in-depth analysis of specific functions of interest (b) identification of microbes contributing to various functions (c) microbial interaction patterns through function-driven correlation networks, and (d) simultaneous functional comparison between multiple microbial communities. We have bench-marked iVikodak through multiple case studies and comparisons with existing state of art. We also introduce the concept of a public repository which provides a first of its kind community-driven framework for scientific data analytics, collaboration and sharing in this area of microbiome research.Conclusion: Developed using modern design and task management practices, iVikodak provides a multi-modular, yet inter-operable, one-stop framework, that intends to simplify the entire approach toward inferred function analysis. It is anticipated to serve as a significant value addition to the existing space of functional metagenomics.iVikodak web-server may be freely accessed at https://web.rniapps.net/iVikodak/

Metagenome of the gut of a malnourished child

<p>Abstract</p> <p>Background</p> <p>Malnutrition, a major health problem, affects a significant proportion of preschool children in developing countries. The devastating consequences of malnutrition include diarrhoea, malabsorption, increased intestinal permeability, suboptimal immune response, etc. Nutritional interventions and dietary solutions have not been effective for treatment of malnutrition till date. Metagenomic procedures allow one to access the complex cross-talk between the gut and its microbial flora and understand how a different community composition affects various states of human health. In this study, a metagenomic approach was employed for analysing the differences between gut microbial communities obtained from a malnourished and an apparently healthy child.</p> <p>Results</p> <p>Our results indicate that the malnourished child gut has an abundance of enteric pathogens which are known to cause intestinal inflammation resulting in malabsorption of nutrients. We also identified a few functional sub-systems from these pathogens, which probably impact the overall metabolic capabilities of the malnourished child gut.</p> <p>Conclusion</p> <p>The present study comprehensively characterizes the microbial community resident in the gut of a malnourished child. This study has attempted to extend the understanding of the basis of malnutrition beyond nutrition deprivation.</p

A cross-sectional study on the nasopharyngeal microbiota of individuals with SARS-CoV-2 infection across three COVID-19 waves in India

BackgroundMultiple variants of the SARS-CoV-2 virus have plagued the world through successive waves of infection over the past three years. Independent research groups across geographies have shown that the microbiome composition in COVID-19 positive patients (CP) differs from that of COVID-19 negative individuals (CN). However, these observations were based on limited-sized sample-sets collected primarily from the early days of the pandemic. Here, we study the nasopharyngeal microbiota in COVID-19 patients, wherein the samples have been collected across the three COVID-19 waves witnessed in India, which were driven by different variants of concern.MethodsThe nasopharyngeal swabs were collected from 589 subjects providing samples for diagnostics purposes at the Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India and subjected to 16s rRNA gene amplicon - based sequencing.FindingsWe found variations in the microbiota of symptomatic vs. asymptomatic COVID-19 patients. CP showed a marked shift in the microbial diversity and composition compared to CN, in a wave-dependent manner. Rickettsiaceae was the only family that was noted to be consistently depleted in CP samples across the waves. The genera Staphylococcus, Anhydrobacter, Thermus, and Aerococcus were observed to be highly abundant in the symptomatic CP patients when compared to the asymptomatic group. In general, we observed a decrease in the burden of opportunistic pathogens in the host microbiota during the later waves of infection.InterpretationTo our knowledge, this is the first analytical cross-sectional study of this scale, which was designed to understand the relation between the evolving nature of the virus and the changes in the human nasopharyngeal microbiota. Although no clear signatures were observed, this study shall pave the way for a better understanding of the disease pathophysiology and help gather preliminary evidence on whether interventions to the host microbiota can help in better protection or faster recovery

Vikodak - A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

<div><p>Background</p><p>The 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.</p><p>Results</p><p>Validation 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.</p><p>Conclusions</p><p>With 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.</p><p>Availability and Implementation</p><p>A web implementation of Vikodak can be publicly accessed at: <a href="http://metagenomics.atc.tcs.com/vikodak" target="_blank">http://metagenomics.atc.tcs.com/vikodak</a>. This web service is freely available for all categories of users (academic as well as commercial).</p></div