Metagenomic Sequence Datasets Research Articles

Evaluation of Experimental Protocols for Shotgun Whole-Genome Metagenomic Discovery of Antibiotic Resistance Genes.

Shotgun metagenomics has enabled the discovery of antibiotic resistance genes (ARGs). Although there have been numerous studies benchmarking the bioinformatics methods for shotgun metagenomic data analysis, there has not yet been a study that systematically evaluates the performance of different experimental protocols on metagenomic species profiling and ARG detection. In this study, we generated 35 whole genome shotgun metagenomic sequencing data sets for five samples (three human stool and two microbial standard) using seven experimental protocols (KAPA or Flex kits at 50ng, 10ng, or 5ng input amounts; XT kit at 1ng input amount). Using this comprehensive resource, we evaluated the seven protocols in terms of robust detection of ARGs and microbial abundance estimation at various sequencing depths. We found that the data generated by the seven protocols are largely similar. The inter-protocol variability is significantly smaller than the variability between samples or sequencing depths. We found that a sequencing depth of more than 30M is suitable for human stool samples. A higher input amount (50ng) is generally favorable for the KAPA and Flex kits. This systematic benchmarking study sheds light on the impact of sequencing depth, experimental protocol, and DNA input amount on ARG detection in human stool samples.

MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research

BackgroundThe complex network of interactions occurring between gastrointestinal (GI) and extra-intestinal (EI) parasitic helminths of humans and animals and the resident gut microbial flora is attracting increasing attention from biomedical researchers, because of the likely implications for the pathophysiology of helminth infection and disease. Nevertheless, the vast heterogeneity of study designs and microbial community profiling strategies, and of bioinformatic and biostatistical approaches for analyses of metagenomic sequence datasets hinder the identification of bacterial targets for follow-up experimental investigations of helminth-microbiota cross-talk. Furthermore, comparative analyses of published datasets are made difficult by the unavailability of a unique repository for metagenomic sequence data and associated metadata linked to studies aimed to explore potential changes in the composition of the vertebrate gut microbiota in response to GI and/or EI helminth infections.ResultsHere, we undertake a meta-analysis of available metagenomic sequence data linked to published studies on helminth-microbiota cross-talk in humans and veterinary species using a single bioinformatic pipeline, and introduce the 'MICrobiome HELminth INteractions database' (MICHELINdb), an online resource for mining of published sequence datasets, and corresponding metadata, generated in these investigations.ConclusionsBy increasing data accessibility, we aim to provide the scientific community with a platform to identify gut microbial populations with potential roles in the pathophysiology of helminth disease and parasite-mediated suppression of host inflammatory responses, and facilitate the design of experiments aimed to disentangle the cause(s) and effect(s) of helminth-microbiota relationships.3-P6fXwiUKUizQ4bBEnJ8wVideo abstract.

MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data.

Antimicrobial resistance (AMR) is a threat to global public health and the identification of genetic determinants of AMR is a critical component to epidemiological investigations. High-throughput sequencing (HTS) provides opportunities for investigation of AMR across all microbial genomes in a sample (i.e. the metagenome). Previously, we presented MEGARes, a hand-curated AMR database and annotation structure developed to facilitate the analysis of AMR within metagenomic samples (i.e. the resistome). Along with MEGARes, we released AmrPlusPlus, a bioinformatics pipeline that interfaces with MEGARes to identify and quantify AMR gene accessions contained within a metagenomic sequence dataset. Here, we present MEGARes 2.0 (https://megares.meglab.org), which incorporates previously published resistance sequences for antimicrobial drugs, while also expanding to include published sequences for metal and biocide resistance determinants. In MEGARes 2.0, the nodes of the acyclic hierarchical ontology include four antimicrobial compound types, 57 classes, 220 mechanisms of resistance, and 1,345 gene groups that classify the 7,868 accessions. In addition, we present an updated version of AmrPlusPlus (AMR ++ version 2.0), which improves accuracy of classifications, as well as expanding scalability and usability.

The co-occurrence and co-exclusion of evolving objects in prokaryotes

Throughout evolution, evolving objects (domains, genes, operons etc.) have continuously combined, forming new proteins, gene clusters, and genomes. Horizontal gene transfer, particularly among prokaryotes, has facilitated this combinatorial process. Thus, evolving objects that interact positively or synergistically with each other are expected to co-occur more often than by chance; conversely, evolving objects may avoid co-occurrence, indicating an antagonistic or redundant functionality between objects. In this work, we use methods adapted from graph theory to understand patterns of co-occurrence and exclusion in prokaryotes. We have implemented multi-level graph models in which each node (vertex) is a gene or species connected by an edge (relationship) to another node to display these coincidence relationships. Our method incorporates the phylogenetic distribution and synthenic distances of these genes, and we demonstrate how these concepts can be used to identify conserved clusters of vertical and horizontally inherited units of selection. We apply these multi-level graph models to a variety of datasets including prokaryotic pangenomes, and metagenomic sequencing datasets from human-associated microbial communities. We find evidence for genes that significantly co-occur with each other within each of these datasets; these genetic clusters include objects from characterized biological pathways but also include genes with unknown functions. Further, we identify genes that exclude each other, indicating evolving objects with antagonistic or redundant biological functions. This work represents a different approach to understanding the evolution of prokaryotes and allows us to draw novel hypotheses as to the potential role of these genetic clusters in prokaryote biology.

Structural Characterization and Directed Evolution of a Novel Acetyl Xylan Esterase Reveals Thermostability Determinants of the Carbohydrate Esterase 7 Family.

A hot desert hypolith metagenomic DNA sequence data set was screened in silico for genes annotated as acetyl xylan esterases (AcXEs). One of the genes identified encoded an ∼36-kDa protein (Axe1NaM1). The synthesized gene was cloned and expressed, and the resulting protein was purified. NaM1 was optimally active at pH 8.5 and 30°C and functionally stable at salt concentrations of up to 5 M. The specific activity and catalytic efficiency were 488.9 U mg-1 and 3.26 × 106 M-1 s-1, respectively. The crystal structure of wild-type NaM1 was solved at a resolution of 2.03 Å, and a comparison with the structures and models of more thermostable carbohydrate esterase 7 (CE7) family enzymes and variants of NaM1 from a directed evolution experiment suggests that reduced side-chain volume of protein core residues is relevant to the thermal stability of NaM1. Surprisingly, a single point mutation (N96S) not only resulted in a simultaneous improvement in thermal stability and catalytic efficiency but also increased the acyl moiety substrate range of NaM1.IMPORTANCE AcXEs belong to nine carbohydrate esterase families (CE1 to CE7, CE12, and CE16), of which CE7 enzymes possess a unique and narrow specificity for acetylated substrates. All structurally characterized members of this family are moderately to highly thermostable. The crystal structure of a novel, mesophilic CE7 AcXE (Axe1NaM1), from a soil metagenome, provides a basis for comparisons with thermostable CE7 enzymes. Using error-prone PCR and site-directed mutagenesis, we enhanced both the stability and activity of the mesophilic AcXE. With comparative structural analyses, we have also identified possible thermal stability determinants. These are valuable for understanding the thermal stability of enzymes within this family and as a guide for future protein engineering of CE7 and other α/β hydrolase enzymes.

A novel Chromatiales bacterium is a potential sulfide oxidizer in multiple orders of marine sponges.

Sponges are benthic filter feeders that play pivotal roles in coupling benthic-pelagic processes in the oceans that involve transformation of dissolved and particulate organic carbon and nitrogen into biomass. While the contribution of sponge holobionts to the nitrogen cycle has been recognized in past years, their importance in the sulfur cycle, both oceanic and physiological, has only recently gained attention. Sponges in general, and Theonella swinhoei in particular, harbour a multitude of associated microorganisms that could affect sulfur cycling within the holobiont. We reconstructed the genome of a Chromatiales (class Gammaproteobacteria) bacterium from a metagenomic sequence dataset of a T. swinhoei-associated microbial community. This relatively abundant bacterium has the metabolic capability to oxidize sulfide yet displays reduced metabolic potential suggestive of its lifestyle as an obligatory symbiont. This bacterium was detected in multiple sponge orders, according to similarities in key genes such as 16S rRNA and polyketide synthase genes. Due to its sulfide oxidation metabolism and occurrence in many members of the Porifera phylum, we suggest naming the newly described taxon Candidatus Porisulfidus.

Novel Abundant Oceanic Viruses of Uncultured Marine Group II Euryarchaeota

SummaryMarine group II Euryarchaeota (MG-II) are among the most abundant microbes in oceanic surface waters [1, 2, 3, 4]. So far, however, representatives of MG-II have not been cultivated, and no viruses infecting these organisms have been described. Here, we present complete genomes for three distinct groups of viruses assembled from metagenomic sequence datasets highly enriched for MG-II. These novel viruses, which we denote magroviruses, possess double-stranded DNA genomes of 65 to 100 kilobases in size that encode a structural module characteristic of head-tailed viruses and, unusually for archaeal and bacterial viruses, a nearly complete replication apparatus of apparent archaeal origin. The newly identified magroviruses are widespread and abundant and therefore are likely to be major ecological agents.

Evolutionary and functional implications of hypervariable loci within the skin virome

Localized genomic variability is crucial for the ongoing conflicts between infectious microbes and their hosts. An understanding of evolutionary and adaptive patterns associated with genomic variability will help guide development of vaccines and antimicrobial agents. While most analyses of the human microbiome have focused on taxonomic classification and gene annotation, we investigated genomic variation of skin-associated viral communities. We evaluated patterns of viral genomic variation across 16 healthy human volunteers. Human papillomavirus (HPV) and Staphylococcus phages contained 106 and 465 regions of diversification, or hypervariable loci, respectively. Propionibacterium phage genomes were minimally divergent and contained no hypervariable loci. Genes containing hypervariable loci were involved in functions including host tropism and immune evasion. HPV and Staphylococcus phage hypervariable loci were associated with purifying selection. Amino acid substitution patterns were virus dependent, as were predictions of their phenotypic effects. We identified diversity generating retroelements as one likely mechanism driving hypervariability. We validated these findings in an independently collected skin metagenomic sequence dataset, suggesting that these features of skin virome genomic variability are widespread. Our results highlight the genomic variation landscape of the skin virome and provide a foundation for better understanding community viral evolution and the functional implications of genomic diversification of skin viruses.

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota.

Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host-microbiome interactions.

COGNIZER: A Framework for Functional Annotation of Metagenomic Datasets

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.

DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.

BackgroundContinual progress in next-generation sequencing allows for generating increasingly large metagenomes which are over time or space. Comparing and classifying the metagenomes with different microbial communities is critical. Alignment-free supervised classification is important for discriminating between the multifarious components of metagenomic samples, because it can be accomplished independently of known microbial genomes.ResultsWe propose an alignment-free supervised metagenomic classification method called DectICO. The intrinsic correlation of oligonucleotides provides the feature set, which is selected dynamically using a kernel partial least squares algorithm, and the feature matrices extracted with this set are sequentially employed to train classifiers by support vector machine (SVM). We evaluated the classification performance of DectICO on three actual metagenomic sequencing datasets, two containing deep sequencing metagenomes and one of low coverage. Validation results show that DectICO is powerful, performs well based on long oligonucleotides (i.e., 6-mer to 8-mer), and is more stable and generalized than a sequence-composition-based method. The classifiers trained by our method are more accurate than non-dynamic feature selection methods and a recently published recursive-SVM-based classification approach.ConclusionsThe alignment-free supervised classification method DectICO can accurately classify metagenomic samples without dependence on known microbial genomes. Selecting the ICO dynamically offers better stability and generality compared with sequence-composition-based classification algorithms. Our proposed method provides new insights in metagenomic sample classification.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0753-3) contains supplementary material, which is available to authorized users.

Grid-Assembly: An oligonucleotide composition-based partitioning strategy to aid metagenomic sequence assembly.

Metagenomics approach involves extraction, sequencing and characterization of the genomic content of entire community of microbes present in a given environment. In contrast to genomic data, accurate assembly of metagenomic sequences is a challenging task. Given the huge volume and the diverse taxonomic origin of metagenomic sequences, direct application of single genome assembly methods on metagenomes are likely to not only lead to an immense increase in requirements of computational infrastructure, but also result in the formation of chimeric contigs. A strategy to address the above challenge would be to partition metagenomic sequence datasets into clusters and assemble separately the sequences in individual clusters using any single-genome assembly method. The current study presents such an approach that uses tetranucleotide usage patterns to first represent sequences as points in a three dimensional (3D) space. The 3D space is subsequently partitioned into "Grids". Sequences within overlapping grids are then progressively assembled using any available assembler. We demonstrate the applicability of the current Grid-Assembly method using various categories of assemblers as well as different simulated metagenomic datasets. Validation results indicate that the Grid-Assembly approach helps in improving the overall quality of assembly, in terms of the purity and volume of the assembled contigs.

RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets.

BackgroundFuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck.ResultsTo overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS – Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets.ConclusionsRIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0503-6) contains supplementary material, which is available to authorized users.

DIME: a novel framework for de novo metagenomic sequence assembly.

The recently developed next generation sequencing platforms not only decrease the cost for metagenomics data analysis, but also greatly enlarge the size of metagenomic sequence datasets. A common bottleneck of available assemblers is that the trade-off between the noise of the resulting contigs and the gain in sequence length for better annotation has not been attended enough for large-scale sequencing projects, especially for the datasets with low coverage and a large number of nonoverlapping contigs. To address this limitation and promote both accuracy and efficiency, we develop a novel metagenomic sequence assembly framework, DIME, by taking the DIvide, conquer, and MErge strategies. In addition, we give two MapReduce implementations of DIME, DIME-cap3 and DIME-genovo, on Apache Hadoop platform. For a systematic comparison of the performance of the assembly tasks, we tested DIME and five other popular short read assembly programs, Cap3, Genovo, MetaVelvet, SOAPdenovo, and SPAdes on four synthetic and three real metagenomic sequence datasets with various reads from fifty thousand to a couple million in size. The experimental results demonstrate that our method not only partitions the sequence reads with an extremely high accuracy, but also reconstructs more bases, generates higher quality assembled consensus, and yields higher assembly scores, including corrected N50 and BLAST-score-per-base, than other tools with a nearly theoretical speed-up. Results indicate that DIME offers great improvement in assembly across a range of sequence abundances and thus is robust to decreasing coverage.

Census-based rapid and accurate metagenome taxonomic profiling.

BackgroundUnderstanding the taxonomic composition of a sample, whether from patient, food or environment, is important to several types of studies including pathogen diagnostics, epidemiological studies, biodiversity analysis and food quality regulation. With the decreasing costs of sequencing, metagenomic data is quickly becoming the preferred typed of data for such analysis.ResultsRapidly defining the taxonomic composition (both taxonomic profile and relative frequency) in a metagenomic sequence dataset is challenging because the task of mapping millions of sequence reads from a metagenomic study to a non-redundant nucleotide database such as the NCBI non-redundant nucleotide database (nt) is a computationally intensive task. We have developed a robust subsampling-based algorithm implemented in a tool called CensuScope meant to take a ‘sneak peak’ into the population distribution and estimate taxonomic composition as if a census was taken of the metagenomic landscape. CensuScope is a rapid and accurate metagenome taxonomic profiling tool that randomly extracts a small number of reads (based on user input) and maps them to NCBI’s nt database. This process is repeated multiple times to ascertain the taxonomic composition that is found in majority of the iterations, thereby providing a robust estimate of the population and measures of the accuracy for the results.ConclusionCensuScope can be run on a laptop or on a high-performance computer. Based on our analysis we are able to provide some recommendations in terms of the number of sequence reads to analyze and the number of iterations to use. For example, to quantify taxonomic groups present in the sample at a level of 1% or higher a subsampling size of 250 random reads with 50 iterations yields a statistical power of >99%. Windows and UNIX versions of CensuScope are available for download at https://hive.biochemistry.gwu.edu/dna.cgi?cmd=censuscope. CensuScope is also available through the High-performance Integrated Virtual Environment (HIVE) and can be used in conjunction with other HIVE analysis and visualization tools.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-918) contains supplementary material, which is available to authorized users.

FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus.

A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.

MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets

A key challenge in analyzing metagenomics data pertains to assembly of sequenced DNA fragments (i.e. reads) originating from various microbes in a given environmental sample. Several existing methodologies can assemble reads originating from a single genome. However, these methodologies cannot be applied for efficient assembly of metagenomic sequence datasets. In this study, we present MetaCAA — a clustering-aided methodology which helps in improving the quality of metagenomic sequence assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly. A software implementation of MetaCAA is available at https://metagenomics.atc.tcs.com/MetaCAA.

Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups

With the astonishing rate that genomic and metagenomic sequence data sets are accumulating, there are many reasons to constrain the data analyses. One approach to such constrained analyses is to focus on select subsets of gene families that are particularly well suited for the tasks at hand. Such gene families have generally been referred to as “marker” genes. We are particularly interested in identifying and using such marker genes for phylogenetic and phylogeny-driven ecological studies of microbes and their communities (e.g., construction of species trees, phylogenetic based assignment of metagenomic sequence reads to taxonomic groups, phylogeny-based assessment of alpha- and beta-diversity of microbial communities from metagenomic data). We therefore refer to these as PhyEco (for phylogenetic and phylogenetic ecology) markers. The dual use of these PhyEco markers means that we needed to develop and apply a set of somewhat novel criteria for identification of the best candidates for such markers. The criteria we focused on included universality across the taxa of interest, ability to be used to produce robust phylogenetic trees that reflect as much as possible the evolution of the species from which the genes come, and low variation in copy number across taxa.We describe here an automated protocol for identifying potential PhyEco markers from a set of complete genome sequences. The protocol combines rapid searching, clustering and phylogenetic tree building algorithms to generate protein families that meet the criteria listed above. We report here the identification of PhyEco markers for different taxonomic levels including 40 for “all bacteria and archaea”, 114 for “all bacteria (greatly expanding on the ∼30 commonly used), and 100 s to 1000 s for some of the individual phyla of bacteria. This new list of PhyEco markers should allow much more detailed automated phylogenetic and phylogenetic ecology analyses of these groups than possible previously.

Simon Dafydd Glyn Stephens

<h3>Abstract</h3> Marine Group II Euryarchaeota (MGII) are among the most abundant microbes in the oceanic surface waters. So far, however, representatives of MGII have not been cultivated, and no viruses...

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

BackgroundObtaining 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 in silico 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.ResultsPerformance 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.ConclusionsIn 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.AvailabilityA web-server for the i-rDNA algorithm is available at http://metagenomics.atc.tcs.com/i-rDNA/