Fast identification and removal of sequence contamination from genomic and metagenomic datasets

High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.     

Comparative Metagenomic Analysis of Coral Microbial Communities Using a Reference-Independent Approach

By comparing the SEED and Pfam functional profiles of metagenomes of two Brazilian coral species with 29 datasets that are publicly available, we were able to identify some functions, such as protein secretion systems, that are overrepresented in the metagenomes of corals and may play a role in the establishment and maintenance of bacteria-coral associations. However, only a small percentage of the reads of these metagenomes could be annotated by these reference databases, which may lead to a strong bias in the comparative studies. For this reason, we have searched for identical sequences (99% of nucleotide identity) among these metagenomes in order to perform a reference-independent comparative analysis, and we were able to identify groups of microbial communities that may be under similar selective pressures. The identification of sequences shared among the metagenomes was found to be even better for the identification of groups of communities with similar niche requirements than the traditional analysis of functional profiles. This approach is not only helpful for the investigation of similarities between microbial communities with high proportion of unknown reads, but also enables an indirect overview of gene exchange between communities.     

An experimental metagenome data management and analysis system

The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity for microbial communities, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context. IMG/M is available at http://img.jgi.doe.gov/m.     

Taxonomic and functional microbial signatures of the endemic marine sponge Arenosclera brasiliensis

The endemic marine sponge Arenosclera brasiliensis (Porifera, Demospongiae, Haplosclerida) is a known source of secondary metabolites such as arenosclerins A-C. In the present study, we established the composition of the A. brasiliensis microbiome and the metabolic pathways associated with this community. We used 454 shotgun pyrosequencing to generate approximately 640,000 high-quality sponge-derived sequences (～150 Mb). Clustering analysis including sponge, seawater and twenty-three other metagenomes derived from marine animal microbiomes shows that A. brasiliensis contains a specific microbiome. Fourteen bacterial phyla (including Proteobacteria, Cyanobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Cloroflexi) were consistently found in the A. brasiliensis metagenomes. The A. brasiliensis microbiome is enriched for Betaproteobacteria (e.g., Burkholderia) and Gammaproteobacteria (e.g., Pseudomonas and Alteromonas) compared with the surrounding planktonic microbial communities. Functional analysis based on Rapid Annotation using Subsystem Technology (RAST) indicated that the A. brasiliensis microbiome is enriched for sequences associated with membrane transport and one-carbon metabolism. In addition, there was an overrepresentation of sequences associated with aerobic and anaerobic metabolism as well as the synthesis and degradation of secondary metabolites. This study represents the first analysis of sponge-associated microbial communities via shotgun pyrosequencing, a strategy commonly applied in similar analyses in other marine invertebrate hosts, such as corals and algae. We demonstrate that A. brasiliensis has a unique microbiome that is distinct from that of the surrounding planktonic microbes and from other marine organisms, indicating a species-specific microbiome.     

Substrate type determines metagenomic profiles from diverse chemical habitats

Environmental parameters drive phenotypic and genotypic frequency variations in microbial communities and thus control the extent and structure of microbial diversity. We tested the extent to which microbial community composition changes are controlled by shifting physiochemical properties within a hypersaline lagoon. We sequenced four sediment metagenomes from the Coorong, South Australia from samples which varied in salinity by 99 Practical Salinity Units (PSU), an order of magnitude in ammonia concentration and two orders of magnitude in microbial abundance. Despite the marked divergence in environmental parameters observed between samples, hierarchical clustering of taxonomic and metabolic profiles of these metagenomes showed striking similarity between the samples (>89%). Comparison of these profiles to those derived from a wide variety of publically available datasets demonstrated that the Coorong sediment metagenomes were similar to other sediment, soil, biofilm and microbial mat samples regardless of salinity (>85% similarity). Overall, clustering of solid substrate and water metagenomes into discrete similarity groups based on functional potential indicated that the dichotomy between water and solid matrices is a fundamental determinant of community microbial metabolism that is not masked by salinity, nutrient concentration or microbial abundance.     

PathAligner

MOTIVATION: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.     

MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences

Determining the taxonomic affiliation of sequences assembled from metagenomes remains a major bottleneck that affects research across the fields of environmental, clinical and evolutionary microbiology. Here, we introduce MyTaxa, a homology-based bioinformatics framework to classify metagenomic and genomic sequences with unprecedented accuracy. The distinguishing aspect of MyTaxa is that it employs all genes present in an unknown sequence as classifiers, weighting each gene based on its (predetermined) classifying power at a given taxonomic level and frequency of horizontal gene transfer. MyTaxa also implements a novel classification scheme based on the genome-aggregate average amino acid identity concept to determine the degree of novelty of sequences representing uncharacterized taxa, i.e. whether they represent novel species, genera or phyla. Application of MyTaxa on in silico generated (mock) and real metagenomes of varied read length (100–2000 bp) revealed that it correctly classified at least 5% more sequences than any other tool. The analysis also showed that ∼10% of the assembled sequences from human gut metagenomes represent novel species with no sequenced representatives, several of which were highly abundant in situ such as members of the Prevotella genus. Thus, MyTaxa can find several important applications in microbial identification and diversity studies.     

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.     

Exploration of Noncoding Sequences in Metagenomes

Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 whole metagenomes, including coding and noncoding sequences using the following sequence patterns: (G+C) content, Codon Usage (Cd), Trinucleotide Usage (Tn), and functional assignments for ORF prediction. Herein, we present evidence of a high proportion of noncoding sequences discarded in common similarity-based methods in metagenomics, and the kind of relevant information present in those. We found a high density of trinucleotide repeat sequences (TRS) in noncoding sequences, with a regulatory and adaptive function for metagenome communities. We present associations between trinucleotide values and gene function, where metagenome clustering correlate with microorganism adaptations and kinds of metagenomes. We propose here that noncoding sequences have relevant information to describe metagenomes that could be considered in a whole metagenome analysis in order to improve their organization, classification protocols, and their relation with the environment.     

Discovery of novel pathways for carbohydrate metabolism

Closing the gap between the increasing availability of complete genome sequences and the discovery of novel enzymes in novel metabolic pathways is a significant challenge. Here, we review recent examples of assignment of in vitro enzymatic activities and in vivo metabolic functions to uncharacterized proteins, with a focus on enzymes and metabolic pathways involved in the catabolism and biosynthesis of monosaccharides and polysaccharides. The most effective approaches are based on analyses of sequence-function space in protein families that provide clues for the predictions of the functions of the uncharacterized enzymes. As summarized in this Opinion, this approach allows the discovery of the catabolism of new molecules, new pathways for common molecules, and new enzymatic chemistries.     

Oligonucleotide fingerprint identification for microarray-based pathogen diagnostic assays

MOTIVATION: Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify 'DNA fingerprints', i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach. RESULTS: The presented computational approach is implemented in an integrated high-performance computing (HPC) software tool for oligonucleotide fingerprint identification termed TOFI. We employed TOFI to identify in silico DNA fingerprints for several bacteria and plasmid sequences, which were then experimentally evaluated as potential probes for microarray-based diagnostic assays. Results and analysis of approximately 150 in silico DNA fingerprints for Yersinia pestis and 250 fingerprints for Francisella tularensis are presented. AVAILABILITY: The implemented algorithm is available upon request.     

Metagenomic mining for microbiologists

Microbial ecologists can now start digging into the accumulating mountains of metagenomic data to uncover the occurrence of functional genes and their correlations to microbial community members. Limitations and biases in DNA extraction and sequencing technologies impact sequence distributions, and therefore, have to be considered. However, when comparing metagenomes from widely differing environments, these fluctuations have a relatively minor role in microbial community discrimination. As a consequence, any functional gene or species distribution pattern can be compared among metagenomes originating from various environments and projects. In particular, global comparisons would help to define ecosystem specificities, such as involvement and response to climate change (for example, carbon and nitrogen cycle), human health risks (eg, presence of pathogen species, toxin genes and viruses) and biodegradation capacities. Although not all scientists have easy access to high-throughput sequencing technologies, they do have access to the sequences that have been deposited in databases, and therefore, can begin to intensively mine these metagenomic data to generate hypotheses that can be validated experimentally. Information about metabolic functions and microbial species compositions can already be compared among metagenomes from different ecosystems. These comparisons add to our understanding about microbial adaptation and the role of specific microbes in different ecosystems. Concurrent with the rapid growth of sequencing technologies, we have entered a new age of microbial ecology, which will enable researchers to experimentally confirm putative relationships between microbial functions and community structures.     

Multiple Profile Models Extract Features from Protein Sequence Data and Resolve Functional Diversity of Very Different Protein Families

Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyze sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organization into functional subgroups and residues that characterize the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer-based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.     

In silico assessment of cell-free systems

Cell‐free extract (CFX)‐derived biocatalytic systems are usually embedded in a complex metabolic network, which makes chemical insulation of the production system necessary by removing enzymatic connections. While insulation can be performed by different methods, the identification of potentially disturbing reactions can become a rather lengthy undertaking requiring extensive experimental analysis and literature review. Therefore, a tool for network topology analysis in cell‐free systems was developed based on genome scale metabolic models. Genome scale metabolic models define a potential network topology for living cells, and can be adapted to the characteristics of cell‐free systems by: (i) removal of compartmentalization, (ii) application of different objective functions, (iii) enabling the accumulation of all metabolites, (iv) applying different constraints for substrate supply, and (v) constraining the reaction space through cofactor availability, microarray data, feasible reaction rates, and thermodynamics. The resulting computational tool successfully predicted for Escherichia coli‐derived CFXs a previously identified undesired pathway for dihydroxyacetone phosphate (DHAP) production from adenosine phosphates. The tool was then applied to the identification of potentially interfering pathways to further insulate a DHAP‐producing multi‐enzyme system based on CFX. Biotechnol. Bioeng. 2012; 109: 2620–2629. © 2012 Wiley Periodicals, Inc.     

Analysis of Hydrocarbon-Contaminated Groundwater Metagenomes as Revealed by High-Throughput Sequencing

The tendency for chlorinated aliphatics and aromatic hydrocarbons to accumulate in environments such as groundwater and sediments poses a serious environmental threat. In this study, the metabolic capacity of hydrocarbon (aromatics and chlorinated aliphatics)-contaminated groundwater in the KwaZulu-Natal province of South Africa has been elucidated for the first time by analysis of pyrosequencing data. The taxonomic data revealed that the metagenomes were dominated by the phylum Proteobacteria (mainly Betaproteobacteria). In addition, Flavobacteriales, Sphingobacteria, Burkholderiales, and Rhodocyclales were the predominant orders present in the individual metagenomes. These orders included microorganisms (Flavobacteria, Dechloromonas aromatica RCB, and Azoarcus) involved in the degradation of aromatic compounds and various other hydrocarbons that were present in the groundwater. Although the metabolic reconstruction of the metagenome represented composite cell networks, the information obtained was sufficient to address questions regarding the metabolic potential of the microbial communities and to correlate the data to the contamination profile of the groundwater. Genes involved in the degradation of benzene and benzoate, heavy metal-resistance mechanisms appeared to provide a survival strategy used by the microbial communities. Analysis of the pyrosequencing-derived data revealed that the metagenomes represent complex microbial communities that have adapted to the geochemical conditions of the groundwater as evidenced by the presence of key enzymes/genes conferring resistance to specific contaminants. Thus, pyrosequencing analysis of the metagenomes provided insights into the microbial activities in hydrocarbon-contaminated habitats.     

Physiological homogeneity among the endosymbionts of Riftia pachyptila and Tevnia jerichonana revealed by proteogenomics

The two closely related deep-sea tubeworms Riftia pachyptila and Tevnia jerichonana both rely exclusively on a single species of sulfide-oxidizing endosymbiotic bacteria for their nutrition. They do, however, thrive in markedly different geochemical conditions. A detailed proteogenomic comparison of the endosymbionts coupled with an in situ characterization of the geochemical environment was performed to investigate their roles and expression profiles in the two respective hosts. The metagenomes indicated that the endosymbionts are genotypically highly homogeneous. Gene sequences coding for enzymes of selected key metabolic functions were found to be 99.9% identical. On the proteomic level, the symbionts showed very consistent metabolic profiles, despite distinctly different geochemical conditions at the plume level of the respective hosts. Only a few minor variations were observed in the expression of symbiont enzymes involved in sulfur metabolism, carbon fixation and in the response to oxidative stress. Although these changes correspond to the prevailing environmental situation experienced by each host, our data strongly suggest that the two tubeworm species are able to effectively attenuate differences in habitat conditions, and thus to provide their symbionts with similar micro-environments.     

MMT--a pathway modeling tool for data from rapid sampling experiments

The identification of metabolic regulation is a major concern in metabolic engineering. Metabolic regulation phenomena depend on intracellular compounds such as enzymes, metabolites and cofactors. A complete understanding of metabolic regulation requires quantitative information about these compounds under in vivo conditions. This quantitative knowledge in combination with the known network of metabolic pathways allows the construction of mathematical models that describe the dynamic changes in metabolite concentrations over time. Rapid sampling combined with pulse experiments is a useful tool for the identification of metabolic regulation owing to the transient data they provide. Enzymatic tests in combination with ESI-LC-MS (Electrospray Ionization Liquid Chromatographic Tandem Mass Spectrometry) and HPLC measurements have been used to identify up to 30 metabolites and nucleotides from rapid sampling experiments. A metabolic modeling tool (MMT) that is built on a relational database was developed specifically for analysis of rapid sampling experiments. The tool allows to construct complex pathway models with information stored in the relational database. Parameter fitting and simulation algorithms for the resulting system of Ordinary Differential Equations (ODEs) are part of MMT. Additionally explicit sensitivity functions are calculated. The integration of all necessary algorithms in one tool allows fast model analysis and comparison. Complex models have been developed to describe the central metabolic pathways of Escherichia coli during a glucose pulse experiment.