MetAMOS: a modular and open source metagenomic assembly and analysis pipeline

Biogeographical distribution analysis of hydrocarbon degrading and biosurfactant producing genes suggests that near-equatorial biomes have higher abundance of genes with potential for bioremediation

Background

Bacterial and Archaeal communities have a complex, symbiotic role in crude oil bioremediation. Their biosurfactants and degradation enzymes have been in the spotlight, mainly due to the awareness of ecosystem pollution caused by crude oil accidents and their use. Initially, the scientific community studied the role of individual microbial species by characterizing and optimizing their biosurfactant and oil degradation genes, studying their individual distribution. However, with the advances in genomics, in particular with the use of New-Generation-Sequencing and Metagenomics, it is now possible to have a macro view of the complex pathways related to the symbiotic degradation of hydrocarbons and surfactant production. It is now possible, although more challenging, to obtain the DNA information of an entire microbial community before automatically characterizing it. By characterizing and understanding the interconnected role of microorganisms and the role of degradation and biosurfactant genes in an ecosystem, it becomes possible to develop new biotechnological approaches for bioremediation use. This paper analyzes 46 different metagenome samples, spanning 20 biomes from different geographies obtained from different research projects.

Results

A metagenomics bioinformatics pipeline, focused on the biodegradation and biosurfactant-production pathways, genes and organisms, was applied. Our main results show that: (1) surfactation and degradation are correlated events, and therefore should be studied together; (2) terrestrial biomes present more degradation genes, especially cyclic compounds, and less surfactation genes, when compared to water biomes; and (3) latitude has a significant influence on the diversity of genes involved in biodegradation and biosurfactant production. This suggests that microbiomes found near the equator are richer in genes that have a role in these processes and thus have a higher biotechnological potential.

Conclusion

In this work we have focused on the biogeographical distribution of hydrocarbon degrading and biosurfactant producing genes. Our principle results can be seen as an important step forward in the application of bioremediation techniques, by considering the biostimulation, optimization or manipulation of a starting microbial consortia from the areas with higher degradation and biosurfactant producing genetic diversity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-1077-4) contains supplementary material, which is available to authorized users.

Assembling metagenomes, one community at a time

BACKGROUND

Metagenomics allows unprecedented access to uncultured environmental microorganisms. The analysis of metagenomic sequences facilitates gene prediction and annotation, and enables the assembly of draft genomes, including uncultured members of a community. However, while several platforms have been developed for this critical step, there is currently no clear framework for the assembly of metagenomic sequence data.

RESULTS

To assist with selection of an appropriate metagenome assembler we evaluated the capabilities of nine prominent assembly tools on nine publicly-available environmental metagenomes, as well as three simulated datasets. Overall, we found that SPAdes provided the largest contigs and highest N50 values across 6 of the 9 environmental datasets, followed by MEGAHIT and metaSPAdes. MEGAHIT emerged as a computationally inexpensive alternative to SPAdes, assembling the most complex dataset using less than 500 GB of RAM and within 10 hours.

CONCLUSIONS

We found that assembler choice ultimately depends on the scientific question, the available resources and the bioinformatic competence of the researcher. We provide a concise workflow for the selection of the best assembly tool.

metaSPAdes: a new versatile metagenomic assembler

While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging, thus stifling biological discoveries. Moreover, recent studies revealed that complex bacterial populations may be composed from dozens of related strains, thus further amplifying the challenge of metagenomic assembly. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse data sets.

A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data

The microbiome can be defined as the community of microorganisms that live in a particular environment. Metagenomics is the practice of sequencing DNA from the genomes of all organisms present in a particular sample, and has become a common method for the study of microbiome population structure and function. Increasingly, researchers are finding novel genes encoded within metagenomes, many of which may be of interest to the biotechnology and pharmaceutical industries. However, such “bioprospecting” requires a suite of sophisticated bioinformatics tools to make sense of the data. This review summarizes the most commonly used bioinformatics tools for the assembly and annotation of metagenomic sequence data with the aim of discovering novel genes.

Metagenomic Sequencing for Surveillance of Food- and Waterborne Viral Diseases

A plethora of viruses can be transmitted by the food- and waterborne route. However, their recognition is challenging because of the variety of viruses, heterogeneity of symptoms, the lack of awareness of clinicians, and limited surveillance efforts. Classical food- and waterborne viral disease outbreaks are mainly caused by caliciviruses, but the source of the virus is often not known and the foodborne mode of transmission is difficult to discriminate from human-to-human transmission. Atypical food- and waterborne viral disease can be caused by viruses such as hepatitis A and hepatitis E. In addition, a source of novel emerging viruses with a potential to spread via the food- and waterborne route is the repeated interaction of humans with wildlife. Wildlife-to-human adaptation may give rise to self- limiting outbreaks in some cases, but when fully adjusted to the human host can be devastating. Metagenomic sequencing has been investigated as a promising solution for surveillance purposes as it detects all viruses in a single protocol, delivers additional genomic information for outbreak tracing, and detects novel unknown viruses. Nevertheless, several issues must be addressed to apply metagenomic sequencing in surveillance. First, sample preparation is difficult since the genomic material of viruses is generally overshadowed by host- and bacterial genomes. Second, several data analysis issues hamper the efficient, robust, and automated processing of metagenomic data. Third, interpretation of metagenomic data is hard, because of the lack of general knowledge of the virome in the food chain and the environment. Further developments in virus-specific nucleic acid extraction methods, bioinformatic data processing applications, and unifying data visualization tools are needed to gain insightful surveillance knowledge from suspect food samples.

Fast and simple protein-alignment-guided assembly of orthologous gene families from microbiome sequencing reads

BACKGROUND

Microbiome sequencing projects typically collect tens of millions of short reads per sample. Depending on the goals of the project, the short reads can either be subjected to direct sequence analysis or be assembled into longer contigs. The assembly of whole genomes from metagenomic sequencing reads is a very difficult problem. However, for some questions, only specific genes of interest need to be assembled. This is then a gene-centric assembly where the goal is to assemble reads into contigs for a family of orthologous genes.

METHODS

We present a new method for performing gene-centric assembly, called protein-alignment-guided assembly, and provide an implementation in our metagenome analysis tool MEGAN. Genes are assembled on the fly, based on the alignment of all reads against a protein reference database such as NCBI-nr. Specifically, the user selects a gene family based on a classification such as KEGG and all reads binned to that gene family are assembled.

RESULTS

Using published synthetic community metagenome sequencing reads and a set of 41 gene families, we show that the performance of this approach compares favorably with that of full-featured assemblers and that of a recently published HMM-based gene-centric assembler, both in terms of the number of reference genes detected and of the percentage of reference sequence covered.

CONCLUSIONS

Protein-alignment-guided assembly of orthologous gene families complements whole-metagenome assembly in a new and very useful way.

Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist's Perspective - Not Only Size Matters!

With the constant improvement in cost-efficiency and quality of Next Generation Sequencing technologies, shotgun-sequencing approaches -such as metagenomics- have nowadays become the methods of choice for studying and classifying microorganisms from various habitats. The production of data has dramatically increased over the past years and processing and analysis steps are becoming more and more of a bottleneck. Limiting factors are partly the availability of computational resources, but mainly the bioinformatics expertise in establishing and applying appropriate processing and analysis pipelines. Fortunately, a large diversity of specialized software tools is nowadays available. Nevertheless, choosing the most appropriate methods for answering specific biological questions can be rather challenging, especially for non-bioinformaticians. In order to provide a comprehensive overview and guide for the microbiological scientific community, we assessed the most common and freely available metagenome assembly tools with respect to their output statistics, their sensitivity for low abundant community members and variability in resulting community profiles as well as their ease-of-use. In contrast to the highly anticipated "Critical Assessment of Metagenomic Interpretation" (CAMI) challenge, which uses general mock community-based assembler comparison we here tested assemblers on real Illumina metagenome sequencing data from natural communities of varying complexity sampled from forest soil and algal biofilms. Our observations clearly demonstrate that different assembly tools can prove optimal, depending on the sample type, available computational resources and, most importantly, the specific research goal. In addition, we present detailed descriptions of the underlying principles and pitfalls of publically available assembly tools from a microbiologist's perspective, and provide guidance regarding the user-friendliness, sensitivity and reliability of the resulting phylogenetic profiles.

A molecular portrait of maternal sepsis from Byzantine Troy

Pregnancy complications are poorly represented in the archeological record, despite their importance in contemporary and ancient societies. While excavating a Byzantine cemetery in Troy, we discovered calcified abscesses among a woman’s remains. Scanning electron microscopy of the tissue revealed ‘ghost cells’, resulting from dystrophic calcification, which preserved ancient maternal, fetal and bacterial DNA of a severe infection, likely chorioamnionitis. Gardnerella vaginalis and Staphylococcus saprophyticus dominated the abscesses. Phylogenomic analyses of ancient, historical, and contemporary data showed that G. vaginalis Troy fell within contemporary genetic diversity, whereas S. saprophyticus Troy belongs to a lineage that does not appear to be commonly associated with human disease today. We speculate that the ecology of S. saprophyticus infection may have differed in the ancient world as a result of close contacts between humans and domesticated animals. These results highlight the complex and dynamic interactions with our microbial milieu that underlie severe maternal infections.

DOI:http://dx.doi.org/10.7554/eLife.20983.001

Why and how have some bacteria evolved to cause illness in humans? One way to study bacterial evolution is to search for ancient samples of bacteria and use DNA sequencing technology to investigate how modern bacteria have changed from their ancestors. Understanding the evolution process may help researchers to understand how some bacteria become resistant to the antibiotics designed to kill them.

Complications that occur during pregnancy, including bacterial infections, have long been a major cause of death for women. Now, Devault, Mortimer et al. have been able to sequence the DNA of bacteria found in tissue collected from a woman buried 800 years ago in a cemetery in Troy. Some of the woman’s tissues had been well preserved because they had calcified (probably as the result of infection), which preserved their structure in a mineralized layer. Two mineralized “nodules” in the body appear to be the remains of abscesses. Some of the human DNA in the nodules came from a male, suggesting that the woman was pregnant with a boy and that the abscesses formed in placental tissue.

Sequencing the DNA of the bacteria in the abscess allowed Devault, Mortimer et al. to diagnose the woman’s infection, which was caused by two types of bacteria. One species, called Gardnerella vaginalis, is found in modern pregnancy-related infections. The DNA of the ancient samples was similar to that of modern bacteria. The other bacteria species was an ancient form of Staphylococcus saprophyticus, a type of bacteria that causes urinary tract infections. However, the DNA of the ancient S. saprophyticus bacteria is quite different to that of the bacteria found in modern humans. Instead, their DNA sequence appears more similar to forms of the bacteria that infect currently livestock. As humans lived closely with their livestock at the time the woman lived, her infection may be due to a type of bacteria that passed easily between humans and animals.

Overall, the results suggest that the disease-causing properties of bacteria can arise from a wide range of sources. In addition, Devault, Mortimer et al. have demonstrated that certain types of tissue found in archeological remains are a potential gold mine of information about the evolution of bacteria and other microbes found in the human body.

DOI:http://dx.doi.org/10.7554/eLife.20983.002

[Gut microbiota, the key for a better diet?]

Modern life is associated with changes in gut microbial communities, believed to be involved in the emergence of non-communicable chronic diseases. While there is an increasing effort of the scientific community towards designing microbiota-targeting therapies aiming to restore the microbiota of diseased patients, there is a lack of approaches designed to prevent the disruption of the symbiosis between human and its microbial symbionts. We discuss in this review how new technologies, tools and models will contribute to identify diet-derived health-relevant microbial metabolites, possible targets for dietary recommendations tailored to individuals' physiology, diet, genetics, lifestyle and gut microbiota.

IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses

Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Survey of (Meta)genomic Approaches for Understanding Microbial Community Dynamics

Advancement in the next generation sequencing technologies has led to evolution of the field of genomics and metagenomics in a slim duration with nominal cost at precipitous higher rate. While metagenomics and genomics can be separately used to reveal the culture-independent and culture-based microbial evolution, respectively, (meta)genomics together can be used to demonstrate results at population level revealing in-depth complex community interactions for specific ecotypes. The field of metagenomics which started with answering "who is out there?" based on 16S rRNA gene has evolved immensely with the precise organismal reconstruction at species/strain level from the deeply covered metagenome data outweighing the need to isolate bacteria of which 99% are de facto non-cultivable. In this review we have underlined the appeal of metagenomic-derived genomes in providing insights into the evolutionary patterns, growth dynamics, genome/gene-specific sweeps, and durability of environmental pressures. We have demonstrated the use of culture-based genomics and environmental shotgun metagenome data together to elucidate environment specific genome modulations via metagenomic recruitments in terms of gene loss/gain, accessory and core-genome extent. We further illustrated the benefit of (meta)genomics in the understanding of infectious diseases by deducing the relationship between human microbiota and clinical microbiology. This review summarizes the technological advances in the (meta)genomic strategies using the genome and metagenome datasets together to increase the resolution of microbial population studies.

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

BACKGROUND

Chromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as a viable means of generating data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised.

METHODS

We developed a computational simulation pipeline for metagenomic 3C and Hi-C sequencing to evaluate the accuracy of genomic reconstructions at, above, and below an operationally defined species boundary. We simulated datasets and measured accuracy over a wide range of parameters. Five clustering algorithms were evaluated (2 hard, 3 soft) using an adaptation of the extended B-cubed validation measure.

RESULTS

When all genomes in a sample are below 95% sequence identity, all of the tested clustering algorithms performed well. When sequence data contains genomes above 95% identity (our operational definition of strain-level diversity), a naive soft-clustering extension of the Louvain method achieves the highest performance.

DISCUSSION

Previously, only hard-clustering algorithms have been applied to metagenomic 3C and Hi-C data, yet none of these perform well when strain-level diversity exists in a metagenomic sample. Our simple extension of the Louvain method performed the best in these scenarios, however, accuracy remained well below the levels observed for samples without strain-level diversity. Strain resolution is also highly dependent on the amount of available 3C sequence data, suggesting that depth of sequencing must be carefully considered during experimental design. Finally, there appears to be great scope to improve the accuracy of strain resolution through further algorithm development.

High Throughput Analysis of Integron Gene Cassettes in Wastewater Environments

Integrons are extensively targeted as a proxy for anthropogenic impact in the environment. We developed a novel high-throughput amplicon sequencing pipeline that enables characterization of thousands of integron gene cassette-associated reads, and applied it to acquire a comprehensive overview of gene cassette composition in effluents from wastewater treatment facilities across Europe. Between 38 100 and 172 995 reads per-sample were generated and functionally characterized by screening against nr, SEED, ARDB and β-lactamase databases. Over 75% of the reads were characterized as hypothetical, but thousands were associated with toxin-antitoxin systems, DNA repair, cell membrane function, detoxification and aminoglycoside and β-lactam resistance. Among the reads characterized as β-lactamases, the carbapenemase bla_OXA was dominant in most of the effluents, except for Cyprus and Israel where bla_GES was also abundant. Quantitative PCR assessment of bla_OXA and bla_GES genes in the European effluents revealed similar trends to those displayed in the integron amplicon sequencing pipeline described above, corroborating the robustness of this method and suggesting that these integron-associated genes may be excellent targets for source tracking of effluents in downstream environments. Further application of the above analyses revealed several order-of-magnitude reductions in effluent-associated β-lactamase genes in effluent-saturated soils, suggesting marginal persistence in the soil microbiome.

Metagenomic Assembly: Overview, Challenges and Applications

Advances in sequencing technologies have led to the increased use of high throughput sequencing in characterizing the microbial communities associated with our bodies and our environment. Critical to the analysis of the resulting data are sequence assembly algorithms able to reconstruct genes and organisms from complex mixtures. Metagenomic assembly involves new computational challenges due to the specific characteristics of the metagenomic data. In this survey, we focus on major algorithmic approaches for genome and metagenome assembly, and discuss the new challenges and opportunities afforded by this new field. We also review several applications of metagenome assembly in addressing interesting biological problems.

Diversity in a Polymicrobial Community Revealed by Analysis of Viromes, Endolysins and CRISPR Spacers

The polymicrobial biofilm communities in Mushroom and Octopus Spring in Yellowstone National Park (YNP) are well characterized, yet little is known about the phage populations. Dominant species, Synechococcus sp. JA-2-3B'a(2–13), Synechococcus sp. JA-3-3Ab, Chloroflexus sp. Y-400-fl, and Roseiflexus sp. RS-1, contain multiple CRISPR-Cas arrays, suggesting complex interactions with phage predators. To analyze phage populations from Octopus Spring biofilms, we sequenced a viral enriched fraction. To assemble and analyze phage metagenomic data, we developed a custom module, VIRITAS, implemented within the MetAMOS framework. This module bins contigs into groups based on tetranucleotide frequencies and CRISPR spacer-protospacer matching and ORF calling. Using this pipeline we were able to assemble phage sequences into contigs and bin them into three clusters that corroborated with their potential host range. The virome contained 52,348 predicted ORFs; some were clearly phage-like; 9319 ORFs had a recognizable Pfam domain while the rest were hypothetical. Of the recognized domains with CRISPR spacer matches, was the phage endolysin used by lytic phage to disrupt cells. Analysis of the endolysins present in the thermophilic cyanophage contigs revealed a subset of characterized endolysins as well as a Glyco_hydro_108 (PF05838) domain not previously associated with sequenced cyanophages. A search for CRISPR spacer matches to all identified phage endolysins demonstrated that a majority of endolysin domains were targets. This strategy provides a general way to link host and phage as endolysins are known to be widely distributed in bacteriophage. Endolysins can also provide information about host cell wall composition and have the additional potential to be used as targets for novel therapeutics.

How Clonal Is Clonal? Genome Plasticity across Multicellular Segments of a "Candidatus Marithrix sp." Filament from Sulfidic, Briny Seafloor Sediments in the Gulf of Mexico

“Candidatus Marithrix” is a recently described lineage within the group of large sulfur bacteria (Beggiatoaceae, Gammaproteobacteria). This genus of bacteria comprises vacuolated, attached-living filaments that inhabit the sediment surface around vent and seep sites in the marine environment. A single filament is ca. 100 μm in diameter, several millimeters long, and consists of hundreds of clonal cells, which are considered highly polyploid. Based on these characteristics, “Candidatus Marithrix” was used as a model organism for the assessment of genomic plasticity along segments of a single filament using next generation sequencing to possibly identify hotspots of microevolution. Using six consecutive segments of a single filament sampled from a mud volcano in the Gulf of Mexico, we recovered ca. 90% of the “Candidatus Marithrix” genome in each segment. There was a high level of genome conservation along the filament with average nucleotide identities between 99.98 and 100%. Different approaches to assemble all reads into a complete consensus genome could not fill the gaps. Each of the six segment datasets encoded merely a few hundred unique nucleotides and 5 or less unique genes—the residual content was redundant in all datasets. Besides the overall high genomic identity, we identified a similar number of single nucleotide polymorphisms (SNPs) between the clonal segments, which are comparable to numbers reported for other clonal organisms. An increase of SNPs with greater distance of filament segments was not observed. The polyploidy of the cells was apparent when analyzing the heterogeneity of reads within a segment. Here, a strong increase in single nucleotide variants, or “intrasegmental sequence heterogeneity” (ISH) events, was observed. These sites may represent hotspots for genome plasticity, and possibly microevolution, since two thirds of these variants were not co-localized across the genome copies of the multicellular filament.

Challenges in the analysis of viral metagenomes

Genome sequencing technologies continue to develop with remarkable pace, yet analytical approaches for reconstructing and classifying viral genomes from mixed samples remain limited in their performance and usability. Existing solutions generally target expert users and often have unclear scope, making it challenging to critically evaluate their performance. There is a growing need for intuitive analytical tooling for researchers lacking specialist computing expertise and that is applicable in diverse experimental circumstances. Notable technical challenges have impeded progress; for example, fragments of viral genomes are typically orders of magnitude less abundant than those of host, bacteria, and/or other organisms in clinical and environmental metagenomes; observed viral genomes often deviate considerably from reference genomes demanding use of exhaustive alignment approaches; high intrapopulation viral diversity can lead to ambiguous sequence reconstruction; and finally, the relatively few documented viral reference genomes compared to the estimated number of distinct viral taxa renders classification problematic. Various software tools have been developed to accommodate the unique challenges and use cases associated with characterizing viral sequences; however, the quality of these tools varies, and their use often necessitates computing expertise or access to powerful computers, thus limiting their usefulness to many researchers. In this review, we consider the general and application-specific challenges posed by viral sequencing and analysis, outline the landscape of available tools and methodologies, and propose ways of overcoming the current barriers to effective analysis.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.

GUTSS: An Alignment-Free Sequence Comparison Method for Use in Human Intestinal Microbiome and Fecal Microbiota Transplantation Analysis

BACKGROUND

Comparative analysis of gut microbiomes in clinical studies of human diseases typically rely on identification and quantification of species or genes. In addition to exploring specific functional characteristics of the microbiome and potential significance of species diversity or expansion, microbiome similarity is also calculated to study change in response to therapies directed at altering the microbiome. Established ecological measures of similarity can be constructed from species abundances, however methods for calculating these commonly used ecological measures of similarity directly from whole genome shotgun (WGS) metagenomic sequence are lacking.

RESULTS

We present an alignment-free method for calculating similarity of WGS metagenomic sequences that is analogous to the Bray-Curtis index for species, implemented by the General Utility for Testing Sequence Similarity (GUTSS) software application. This method was applied to intestinal microbiomes of healthy young children to measure developmental changes toward an adult microbiome during the first 3 years of life. We also calculate similarity of donor and recipient microbiomes to measure establishment, or engraftment, of donor microbiota in fecal microbiota transplantation (FMT) studies focused on mild to moderate Crohn's disease. We show how a relative index of similarity to donor can be calculated as a measure of change in a patient's microbiome toward that of the donor in response to FMT.

CONCLUSION

Because clinical efficacy of the transplant procedure cannot be fully evaluated without analysis methods to quantify actual FMT engraftment, we developed a method for detecting change in the gut microbiome that is independent of species identification and database bias, sensitive to changes in relative abundance of the microbial constituents, and can be formulated as an index for correlating engraftment success with clinical measures of disease. More generally, this method may be applied to clinical evaluation of human microbiomes and provide potential diagnostic determination of individuals who may be candidates for specific therapies directed at alteration of the microbiome.

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce.

MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data

MOTIVATION

High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches were used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features.

RESULTS

We developed MetaFast, an approach that allows to represent a shotgun metagenome from an arbitrary environment as a modified de Bruijn graph consisting of simplified components. For multiple metagenomes, the resulting representation is used to obtain a pairwise similarity matrix. The dimensional structure of the metagenomic components preserved in our algorithm reflects the inherent subspecies-level diversity of microbiota. The method is computationally efficient and especially promising for an analysis of metagenomes from novel environmental niches.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available for download at https://github.com/ctlab/metafast The code is written in Java and is platform independent (tested on Linux and Windows x86_64).

CONTACT

ulyantsev@rain.ifmo.ru

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics

The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses. Several methods for taxonomic classification of bacterial sequences are discussed. We present simulations to assess the number of sequences that are required to perform reliable appraisals of bacterial community structure. To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity). Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.

Identification and Genomic Analysis of a Novel Group C Orthobunyavirus Isolated from a Mosquito Captured near Iquitos, Peru

Group C orthobunyaviruses are single-stranded RNA viruses found in both South and North America. Until very recently, and despite their status as important vector-borne human pathogens, no Group C whole genome sequences containing all three segments were available in public databases. Here we report a Group C orthobunyavirus, named El Huayo virus, isolated from a pool of Culex portesi mosquitoes captured near Iquitos, Peru. Although initial metagenomic analysis yielded only a handful of reads belonging to the genus Orthobunyavirus, single contig assemblies were generated for L, M, and S segments totaling over 200,000 reads (~0.5% of sample). Given the moderately high viremia in hamsters (>107 plaque-forming units/ml) and the propensity for Cx. portesi to feed on rodents, it is possible that El Huayo virus is maintained in nature in a Culex portesi/rodent cycle. El Huayo virus was found to be most similar to Peruvian Caraparu virus isolates and constitutes a novel subclade within Group C.

MG-Digger: An Automated Pipeline to Search for Giant Virus-Related Sequences in Metagenomes

The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human metagenomes include a significant amount of non-annotated sequences, representing a 'dark matter.' We established a bioinformatics pipeline that automatically detects metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human metagenomes (≈ 1 Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral metagenome analysis, MG-Digger detected 8% more true positive Megavirales-related reads in a control metagenome. The present work shows that massive, automated and recurrent analyses of metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

TruSPAdes: barcode assembly of TruSeq synthetic long reads

The recently introduced TruSeq synthetic long read (TSLR) technology generates long and accurate virtual reads from an assembly of barcoded pools of short reads. The TSLR method provides an attractive alternative to existing sequencing platforms that generate long but inaccurate reads. We describe the truSPAdes algorithm (http://bioinf.spbau.ru/spades) for TSLR assembly and show that it results in a dramatic improvement in the quality of metagenomics assemblies.

Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome

Identifying bacterial strains in metagenome and microbiome samples using computational analyses of short-read sequence remains a difficult problem. Here, we present an analysis of a human gut microbiome using on Tru-seq synthetic long reads combined with new computational tools for metagenomic long-read assembly, variant-calling and haplotyping (Nanoscope and Lens). Our analysis identifies 178 bacterial species of which 51 were not found using short sequence reads alone. We recover bacterial contigs that comprise multiple operons, including 22 contigs of >1Mbp. Extensive intraspecies variation among microbial strains in the form of haplotypes that span up to hundreds of Kbp can be observed using our approach. Our method incorporates synthetic long-read sequencing technology with standard shotgun approaches to move towards rapid, precise and comprehensive analyses of metagenome and microbiome samples.

VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires

Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.

Metagenomics: Retrospect and Prospects in High Throughput Age

In recent years, metagenomics has emerged as a powerful tool for mining of hidden microbial treasure in a culture independent manner. In the last two decades, metagenomics has been applied extensively to exploit concealed potential of microbial communities from almost all sorts of habitats. A brief historic progress made over the period is discussed in terms of origin of metagenomics to its current state and also the discovery of novel biological functions of commercial importance from metagenomes of diverse habitats. The present review also highlights the paradigm shift of metagenomics from basic study of community composition to insight into the microbial community dynamics for harnessing the full potential of uncultured microbes with more emphasis on the implication of breakthrough developments, namely, Next Generation Sequencing, advanced bioinformatics tools, and systems biology.

Reconstructing 16S rRNA genes in metagenomic data

Metagenomic data, which contains sequenced DNA reads of uncultured microbial species from environmental samples, provide a unique opportunity to thoroughly analyze microbial species that have never been identified before. Reconstructing 16S ribosomal RNA, a phylogenetic marker gene, is usually required to analyze the composition of the metagenomic data. However, massive volume of dataset, high sequence similarity between related species, skewed microbial abundance and lack of reference genes make 16S rRNA reconstruction difficult. Generic de novo assembly tools are not optimized for assembling 16S rRNA genes. In this work, we introduce a targeted rRNA assembly tool, REAGO (REconstruct 16S ribosomal RNA Genes from metagenOmic data). It addresses the above challenges by combining secondary structure-aware homology search, zproperties of rRNA genes and de novo assembly. Our experimental results show that our tool can correctly recover more rRNA genes than several popular generic metagenomic assembly tools and specially designed rRNA construction tools.

AVAILABILITY AND IMPLEMENTATION

The source code of REAGO is freely available at https://github.com/chengyuan/reago.

Subtractive assembly for comparative metagenomics, and its application to type 2 diabetes metagenomes

Comparative metagenomics remains challenging due to the size and complexity of metagenomic datasets. Here we introduce subtractive assembly, a de novo assembly approach for comparative metagenomics that directly assembles only the differential reads that distinguish between two groups of metagenomes. Using simulated datasets, we show it improves both the efficiency of the assembly and the assembly quality of the differential genomes and genes. Further, its application to type 2 diabetes (T2D) metagenomic datasets reveals clear signatures of the T2D gut microbiome, revealing new phylogenetic and functional features of the gut microbial communities associated with T2D.

Molecular characterization of the human microbiome from a reproductive perspective

The process of reproduction inherently poses unique microbial challenges because it requires the transfer of gametes from one individual to the other, meanwhile preserving the integrity of the gametes and individuals from harmful microbes during the process. Advances in molecular biology techniques have expanded our understanding of the natural organisms living on and in our bodies, including those inhabiting the reproductive tract. Over the past two decades accumulating evidence has shown that the human microbiome is tightly related to health and disease states involving the different body systems, including the reproductive system. Here we introduce the science involved in the study of the human microbiome. We examine common methods currently used to characterize the human microbiome as an inseparable part of the reproductive system. Finally, we consider a few limitations, clinical implications, and the critical need for additional research in the field of human fertility.

Anvi'o: an advanced analysis and visualization platform for 'omics data

Advances in high-throughput sequencing and ‘omics technologies are revolutionizing studies of naturally occurring microbial communities. Comprehensive investigations of microbial lifestyles require the ability to interactively organize and visualize genetic information and to incorporate subtle differences that enable greater resolution of complex data. Here we introduce anvi’o, an advanced analysis and visualization platform that offers automated and human-guided characterization of microbial genomes in metagenomic assemblies, with interactive interfaces that can link ‘omics data from multiple sources into a single, intuitive display. Its extensible visualization approach distills multiple dimensions of information about each contig, offering a dynamic and unified work environment for data exploration, manipulation, and reporting. Using anvi’o, we re-analyzed publicly available datasets and explored temporal genomic changes within naturally occurring microbial populations through de novo characterization of single nucleotide variations, and linked cultivar and single-cell genomes with metagenomic and metatranscriptomic data. Anvi’o is an open-source platform that empowers researchers without extensive bioinformatics skills to perform and communicate in-depth analyses on large ‘omics datasets.

Structure and function of the healthy pre-adolescent pediatric gut microbiome

BACKGROUND

The gut microbiome influences myriad host functions, including nutrient acquisition, immune modulation, brain development, and behavior. Although human gut microbiota are recognized to change as we age, information regarding the structure and function of the gut microbiome during childhood is limited. Using 16S rRNA gene and shotgun metagenomic sequencing, we characterized the structure, function, and variation of the healthy pediatric gut microbiome in a cohort of school-aged, pre-adolescent children (ages 7-12 years). We compared the healthy pediatric gut microbiome with that of healthy adults previously recruited from the same region (Houston, TX, USA).

RESULTS

Although healthy children and adults harbored similar numbers of taxa and functional genes, their composition and functional potential differed significantly. Children were enriched in Bifidobacterium spp., Faecalibacterium spp., and members of the Lachnospiraceae, while adults harbored greater abundances of Bacteroides spp. From a functional perspective, significant differences were detected with respect to the relative abundances of genes involved in vitamin synthesis, amino acid degradation, oxidative phosphorylation, and triggering mucosal inflammation. Children's gut communities were enriched in functions which may support ongoing development, while adult communities were enriched in functions associated with inflammation, obesity, and increased risk of adiposity.

CONCLUSIONS

Previous studies suggest that the human gut microbiome is relatively stable and adult-like after the first 1 to 3 years of life. Our results suggest that the healthy pediatric gut microbiome harbors compositional and functional qualities that differ from those of healthy adults and that the gut microbiome may undergo a more prolonged development than previously suspected.

InteMAP: Integrated metagenomic assembly pipeline for NGS short reads

Background

Next-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed.

Results

We conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others.

Conclusions

We developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0686-x) contains supplementary material, which is available to authorized users.

The use of next generation sequencing in the diagnosis and typing of respiratory infections

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We compared current viral respiratory diagnostic techniques with NGS.

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NGS is able to detect respiratory viruses in clinical diagnostic samples.

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With the current sample preparation method, NGS is less sensitive than RT-PCR.

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NGS provided additional sequence and typing information compared with RT-PCR.

Background

Molecular assays are the gold standard methods used to diagnose viral respiratory pathogens. Pitfalls associated with this technique include limits to the number of targeted pathogens, the requirement for continuous monitoring to ensure sensitivity/specificity is maintained and the need to evolve to include emerging pathogens. Introducing target independent next generation sequencing (NGS) could resolve these issues and revolutionise respiratory viral diagnostics.

Objectives

To compare the sensitivity and specificity of target independent NGS against the current standard diagnostic test.

Study design

Diagnostic RT-PCR of clinical samples was carried out in parallel with target independent NGS. NGS sequences were analyzed to determine the proportion with viral origin and consensus sequences were used to establish viral genotypes and serotypes where applicable.

Results

89 nasopharyngeal swabs were tested. A viral pathogen was detected in 43% of samples by NGS and 54% by RT-PCR. All NGS viral detections were confirmed by RT-PCR.

Conclusions

Target independent NGS can detect viral pathogens in clinical samples. Where viruses were detected by RT-PCR alone the Ct value was higher than those detected by both assays, suggesting an NGS detection cut-off – Ct = 32. The sensitivity and specificity of NGS compared with RT-PCR was 78% and 80% respectively. This is lower than current diagnostic assays but NGS provided full genome sequences in some cases, allowing determination of viral subtype and serotype. Sequencing technology is improving rapidly and it is likely that within a short period of time sequencing depth will increase in-turn improving test sensitivity.

Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome

The advances in experimental methods and the development of high performance bioinformatic tools have substantially improved our understanding of microbial communities associated with human niches. Many studies have documented that changes in microbial abundance and composition of the human microbiome is associated with human health and diseased state. The majority of research on human microbiome is typically focused in the analysis of one level of biological information, i.e., metagenomics or metatranscriptomics. In this review, we describe some of the different experimental and bioinformatic strategies applied to analyze the 16S rRNA gene profiling and shotgun sequencing data of the human microbiome. We also discuss how some of the recent insights in the combination of metagenomics, metatranscriptomics and viromics can provide more detailed description on the interactions between microorganisms and viruses in oral and gut microbiomes. Recent studies on viromics have begun to gain importance due to the potential involvement of viruses in microbial dysbiosis. In addition, metatranscriptomic combined with metagenomic analysis have shown that a substantial fraction of microbial transcripts can be differentially regulated relative to their microbial genomic abundances. Thus, understanding the molecular interactions in the microbiome using the combination of metagenomics, metatranscriptomics and viromics is one of the main challenges towards a system level understanding of human microbiome.

Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of "metagenomics", often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.

Insights from the metagenome of an acid salt lake: the role of biology in an extreme depositional environment

The extremely acidic brine lakes of the Yilgarn Craton of Western Australia are home to some of the most biologically challenging waters on Earth. In this study, we employed metagenomic shotgun sequencing to generate a microbial profile of the depositional environment associated with the sulfur-rich sediments of one such lake. Of the 1.5 M high-quality reads generated, 0.25 M were mapped to protein features, which in turn provide new insights into the metabolic function of this community. In particular, 45 diverse genes associated with sulfur metabolism were identified, the majority of which were linked to either the conversion of sulfate to adenylylsulfate and the subsequent production of sulfide from sulfite or the oxidation of sulfide, elemental sulfur, and thiosulfate via the sulfur oxidation (Sox) system. This is the first metagenomic study of an acidic, hypersaline depositional environment, and we present evidence for a surprisingly high level of microbial diversity. Our findings also illuminate the possibility that we may be meaningfully underestimating the effects of biology on the chemistry of these sulfur-rich sediments, thereby influencing our understanding of past geobiological conditions that may have been present on Earth as well as early Mars.

Thermophiles in the genomic era: Biodiversity, science, and applications

Thermophiles and hyperthermophiles are present in various regions of the Earth, including volcanic environments, hot springs, mud pots, fumaroles, geysers, coastal thermal springs, and even deep-sea hydrothermal vents. They are also found in man-made environments, such as heated compost facilities, reactors, and spray dryers. Thermophiles, hyperthermophiles, and their bioproducts facilitate various industrial, agricultural, and medicinal applications and offer potential solutions to environmental damages and the demand for biofuels. Intensified efforts to sequence the entire genome of hyperthermophiles and thermophiles are increasing rapidly, as evidenced by the fact that over 120 complete genome sequences of the hyperthermophiles Aquificae, Thermotogae, Crenarchaeota, and Euryarchaeota are now available. In this review, we summarise the major current applications of thermophiles and thermozymes. In addition, emphasis is placed on recent progress in understanding the biodiversity, genomes, transcriptomes, metagenomes, and single-cell sequencing of thermophiles in the genomic era.

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.

The Gastrointestinal Microbiome: Alcohol Effects on the Composition of Intestinal Microbiota

The excessive use of alcohol is a global problem causing many adverse pathological health effects and a significant financial health care burden. This review addresses the effect of alcohol consumption on the microbiota in the gastrointestinal tract (GIT). Although data are limited in humans, studies highlight the importance of changes in the intestinal microbiota in alcohol-related disorders. Alcohol-induced changes in the GIT microbiota composition and metabolic function may contribute to the well-established link between alcohol-induced oxidative stress, intestinal hyperpermeability to luminal bacterial products, and the subsequent development of alcoholic liver disease (ALD), as well as other diseases. In addition, clinical and preclinical data suggest that alcohol-related disorders are associated with quantitative and qualitative dysbiotic changes in the intestinal microbiota and may be associated with increased GIT inflammation, intestinal hyperpermeability resulting in endotoxemia, systemic inflammation, and tissue damage/organ pathologies including ALD. Thus, gut-directed interventions, such as probiotic and synbiotic modulation of the intestinal microbiota, should be considered and evaluated for prevention and treatment of alcohol-associated pathologies.

Microbial Forensics☆

Biothreats are a high priority concern for public safety and national security. The field of microbial forensics was developed to analyze evidence associated with biological crimes in which microbes or their toxins are used as weapons. Microbial forensics is the scientific discipline dedicated to analyzing evidence from a bioterrorism act, biocrime, hoax, or inadvertent microorganism/toxin release for attribution purposes. Microbial forensics combines the practices of epidemiology with the characterization of microbial and microbial-related evidence to assist in determining the specific source of the sample, as individualizing as possible, and/or the methods, means, processes and locations involved to determine the identity of the perpetrator(s) of an attack.

Phylogenetics and the human microbiome

The human microbiome is the ensemble of genes in the microbes that live inside and on the surface of humans. Because microbial sequencing information is now much easier to come by than phenotypic information, there has been an explosion of sequencing and genetic analysis of microbiome samples. Much of the analytical work for these sequences involves phylogenetics, at least indirectly, but methodology has developed in a somewhat different direction than for other applications of phylogenetics. In this article, I review the field and its methods from the perspective of a phylogeneticist, as well as describing current challenges for phylogenetics coming from this type of work.

Next-Generation Sequencing: A Review of Technologies and Tools for Wound Microbiome Research

Significance: The colonization of wounds by specific microbes or communities of microbes may delay healing and/or lead to infection-related complication. Studies of wound-associated microbial communities (microbiomes) to date have primarily relied upon culture-based methods, which are known to have extreme biases and are not reliable for the characterization of microbiomes. Biofilms are very resistant to culture and are therefore especially difficult to study with techniques that remain standard in clinical settings. Recent Advances: Culture-independent approaches employing next-generation DNA sequencing have provided researchers and clinicians a window into wound-associated microbiomes that could not be achieved before and has begun to transform our view of wound-associated biodiversity. Within the past decade, many platforms have arisen for performing this type of sequencing, with various types of applications for microbiome research being possible on each. Critical Issues: Wound care incorporating knowledge of microbiomes gained from next-generation sequencing could guide clinical management and treatments. The purpose of this review is to outline the current platforms, their applications, and the steps necessary to undertake microbiome studies using next-generation sequencing. Future Directions: As DNA sequencing technology progresses, platforms will continue to produce longer reads and more reads per run at lower costs. A major future challenge is to implement these technologies in clinical settings for more precise and rapid identification of wound bioburden.