Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes

Are the predicted known bacterial strains in a sample really present? A case study

With mutations constantly accumulating in bacterial genomes, it is unclear whether the previously identified bacterial strains are really present in an extant sample. To address this question, we did a case study on the known strains of the bacterial species S. aureus and S. epidermis in 68 atopic dermatitis shotgun metagenomic samples. We evaluated the likelihood of the presence of all sixteen known strains predicted in the original study and by two popular tools in this study. We found that even with the same tool, only two known strains were predicted by the original study and this study. Moreover, none of the sixteen known strains was likely present in these 68 samples. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. It implies the unlikely presence of the previously identified known strains in a current environmental sample. It also called for de novo bacterial strain identification directly from shotgun metagenomic reads.

Legionella relative abundance in shower hose biofilms is associated with specific microbiome members

Legionella are natural inhabitants of building plumbing biofilms, where interactions with other microorganisms influence their survival, proliferation, and death. Here, we investigated the associations of Legionella with bacterial and eukaryotic microbiomes in biofilm samples extracted from 85 shower hoses of a multiunit residential building. Legionella spp. relative abundance in the biofilms ranged between 0-7.8%, of which only 0-0.46% was L. pneumophila. Our data suggest that some microbiome members were associated with high (e.g. Chthonomonas, Vrihiamoeba) or low (e.g. Aquabacterium, Vannella) Legionella relative abundance. The correlations of the different Legionella variants (30 Zero-Radius OTUs detected) showed distinct patterns, suggesting separate ecological niches occupied by different Legionella species. This study provides insights into the ecology of Legionella with respect to: (i) the colonization of a high number of real shower hoses biofilm samples; (ii) the ecological meaning of associations between Legionella and co-occurring bacterial/eukaryotic organisms; (iii) critical points and future directions of microbial-interaction-based-ecological-investigations.

A fast machine-learning-guided primer design pipeline for selective whole genome amplification

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primer sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

A Taxonomy-Agnostic Approach to Targeted Microbiome Therapeutics-Leveraging Principles of Systems Biology

The study of human microbiomes has yielded insights into basic science, and applied therapeutics are emerging. However, conflicting definitions of what microbiomes are and how they affect the health of the "host" are less understood. A major impediment towards systematic design, discovery, and implementation of targeted microbiome therapeutics is the continued reliance on taxonomic indicators to define microbiomes in health and disease. Such reliance often confounds analyses, potentially suggesting associations where there are none, and conversely failing to identify significant, causal relationships. This review article discusses recent discoveries pointing towards a molecular understanding of microbiome "dysbiosis" and away from a purely taxonomic approach. We highlight the growing role of systems biological principles in the complex interrelationships between the gut microbiome and host cells, and review current approaches commonly used in targeted microbiome therapeutics, including fecal microbial transplant, bacteriophage therapies, and the use of metabolic toxins to selectively eliminate specific taxa from dysbiotic microbiomes. These approaches, however, remain wholly or partially dependent on the bacterial taxa involved in dysbiosis, and therefore may not capitalize fully on many therapeutic opportunities presented at the bioactive molecular level. New technologies capable of addressing microbiome-associated diseases as molecular problems, if solved, will open possibilities of new classes and categories of targeted microbiome therapeutics aimed, in principle, at all dysbiosis-driven disorders.

Determining the diet of wild Asian elephants (Elephasmaximus) at human-elephant conflict areas in Peninsular Malaysia using DNA metabarcoding

Human-elephant conflict (HEC) contributes to the increasing death of Asian elephants due to road accidents, retaliatory killings and fatal infections from being trapped in snares. Understanding the diet of elephants throughout Peninsular Malaysia remains crucial to improve their habitat quality and reduce scenarios of HEC. DNA metabarcoding allows investigating the diet of animals without direct observation, especially in risky conflict areas. The aim of this study was to determine: i) the diet of wild Asian elephants from HEC areas in Peninsular Malaysia using DNA metabarcoding and ii) the influence of distinct environmental parameters at HEC locations on their feeding patterns. DNA was extracted from 39 faecal samples and pooled into 12 groups representing the different sample locations: Kuala Koh, Kenyir, Ulu Muda, Sira Batu, Kupang-Grik, Bumbun Tahan, Belum-Temengor, Grik, Kampung Pagi, Kampung Kuala Balah, Aring 10 and the National Elephant Conservation Centre, which served as a positive control for this study. DNA amplification and sequencing targeted the ribulose-bisphosphate carboxylase gene using the next-generation sequencing Illumina iSeq100 platform. Overall, we identified 35 orders, 88 families, 196 genera and 237 species of plants in the diet of the Asian elephants at HEC hotspots. Ficus (Moraceae), Curcuma (Zingiberaceae), Phoenix (Arecaceae), Maackia (Fabaceae), Garcinia (Clusiaceae) and Dichapetalum (Dichapetalaceae) were the highly abundant dietary plants. The plants successfully identified in this study could be used by the Department of Wildlife and National Parks (PERHILITAN) to create buffer zones by planting the recommended dietary plants around HEC locations and trails of elephants within Central Forest Spine (CFS) landscape.

A revisit to universal single-copy genes in bacterial genomes

Universal single-copy genes (USCGs) are widely used for species classification and taxonomic profiling. Despite many studies on USCGs, our understanding of USCGs in bacterial genomes might be out of date, especially how different the USCGs are in different studies, how well a set of USCGs can distinguish two bacterial species, whether USCGs can separate different strains of a bacterial species, to name a few. To fill the void, we studied USCGs in the most updated complete bacterial genomes. We showed that different USCG sets are quite different while coming from highly similar functional categories. We also found that although USCGs occur once in almost all bacterial genomes, each USCG does occur multiple times in certain genomes. We demonstrated that USCGs are reliable markers to distinguish different species while they cannot distinguish different strains of most bacterial species. Our study sheds new light on the usage and limitations of USCGs, which will facilitate their applications in evolutionary, phylogenomic, and metagenomic studies.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Environmental DNA from Marine Waters and Substrates: Protocols for Sampling and eDNA Extraction

Environmental DNA (eDNA) analysis has emerged in recent years as a powerful tool for the detection, monitoring, and characterization of aquatic metazoan communities, including vulnerable species. The rapid rate of adopting the eDNA approach across diverse habitats and taxonomic groups attests to its value for a wide array of investigative goals, from understanding natural or changing biodiversity to informing on conservation efforts at local and global scales. Regardless of research objectives, eDNA workflows commonly include the following essential steps: environmental sample acquisition, processing and preservation of samples, and eDNA extraction, followed by eDNA sequencing library preparation, high-capacity sequencing and sequence data analysis, or other methods of genetic detection. In this chapter, we supply instructional details for the early steps in the workflow to facilitate researchers considering adopting eDNA analysis to address questions in marine environments. Specifically, we detail sampling, preservation, extraction, and quantification protocols for eDNA originating from marine water, shallow substrates, and deeper sediments. eDNA is prone to degradation and loss, and to contamination through improper handling; these factors crucially influence the outcome and validity of an eDNA study. Thus, we also provide guidance on avoiding these pitfalls. Following extraction, purified eDNA is often sequenced on massively parallel sequencing platforms for comprehensive faunal diversity assessment using a metabarcoding or metagenomic approach, or for the detection and quantification of specific taxa by qPCR methods. These components of the workflow are project-specific and thus not included in this chapter. Instead, we briefly touch on the preparation of eDNA libraries and discuss comparisons between sequencing approaches to aid considerations in project design.

K2Mem: Discovering Discriminative K-mers From Sequencing Data for Metagenomic Reads Classification

The major problem when analyzing a metagenomic sample is to taxonomically annotate its reads to identify the species they contain. Most of the methods currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of recall (the actual number of classified reads) the performances fall at around 50%. One of the reasons is the fact that the sequences in a sample can be very different from the corresponding reference genome, e.g., viral genomes are highly mutated. To address this issue, in this paper we study the problem of metagenomic reads classification by improving the reference k-mers library with novel discriminative k-mers from the input sequencing reads. We evaluated the performance in different conditions against several other tools and the results showed an improved F-measure, especially when close reference genomes are not available. Availability: https://github.com.

Genetic detection of freshwater harmful algal blooms: A review focused on the use of environmental DNA (eDNA) in Microcystis aeruginosa and Prymnesium parvum

Recurrence and severity of harmful algal blooms (HABs) are increasing due to a number of factors, including human practices and climate change. Sensitive and robust methods that allow for early and expedited HAB detection across large landscape scales are needed. Among the suite of HAB detection tools available, a powerful option exists in genetics-based approaches utilizing environmental sampling, also termed environmental DNA (eDNA). Here we provide a detailed methodological review of three HAB eDNA approaches (quantitative PCR, high throughput sequencing, and isothermal amplification). We then summarize and synthesize recently published eDNA applications covering a variety of HAB surveillance and research objectives, all with a specific emphasis in the detection of two widely problematic freshwater species, Microcystis aeruginosa and Prymnesium parvum. In our summary and conclusion we build on this literature by discussing ways in which eDNA methods could be advanced to improve HAB detection. We also discuss ways in which eDNA data could be used to potentially provide novel insight into the ecology, mitigation, and prediction of HABs.

Occupational exposure to anaerobic bacteria in a waste sorting plant

The study focused on exposure assessment to bacterial aerosols and organic dust in waste sorting plant. Samples were collected at different workplaces of waste sorting cycle i.e.: waste press, reloading area, loading of conveyor belt, sorting cabin, sorting hall, and control room. A quantitative analysis of aerobic and anaerobic bacteria was supplemented by qualitative analysis of anaerobic biota with the use of culture-based methods and biochemical tests. In addition, inhalable dust concentrations were also evaluated. To confirm the presence of Clostridium genus, the PCR reaction with specific primers (Chis150f and ClostIr) was performed. The average concentration of total bacteria in waste sorting plant was 4347 CFU m^-3 (SD = 2439), of which 66% were anaerobic strains (2852 CFU m^-3; SD = 2127). It was found that about 24% of anaerobic bacteria belonged to Clostridium genus (682 CFU m^-3; SD = 633). The highest contamination with anaerobic bacteria was observed near the waste reloading plant (3740 CFU m^-3), and the lowest in the control room (850 CFU m^-3). The average concentration of inhalable dust in the waste sorting plant was 0.81 mg m^-3 (SD = 0.59). The correlation analysis showed that the presence of anaerobic bacteria, including clostridia was significantly determined by the microclimate parameters. Qualitative analysis showed the presence of 16 anaerobic species belonging to 9 genera, of which Actinomyces, Clostridium, and Gemella were present at all workplaces. The molecular analysis confirmed the presence of Clostridium genus in both bioaerosol and settled dust samples.Implications: The study showed that anaerobic bacteria should be taken into account as an important component of this microbiota when assessing the exposure of waste sorting workers to biological agents. However, future studies should investigate more precisely how the composition of sorted waste as well as the season can affect the diversity of anaerobic bacteria in this working environment. More attention should be paid to regular cleaning of equipment surfaces in the plant, as deposited organic dust is an important reservoir of anaerobic bacteria, including those of a potentially pathogenic nature.

MetaProb 2: Metagenomic Reads Binning Based on Assembly Using Minimizers and K-Mers Statistics

Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. The major difficulties of taxonomic analysis are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, and sequencing errors. Microbial communities can be studied with reads clustering, a process referred to as genome binning. In this study, we present MetaProb 2 an unsupervised genome binning method based on reads assembly and probabilistic k-mers statistics. The novelties of MetaProb 2 are the use of minimizers to efficiently assemble reads into unitigs and a community detection algorithm based on graph modularity to cluster unitigs and to detect representative unitigs. The effectiveness of MetaProb 2 is demonstrated in both simulated and real datasets in comparison with state-of-art binning tools such as MetaProb, AbundanceBin, Bimeta, and MetaCluster. On real datasets, it is the only one capable of producing promising results while being parsimonious with computational resources.

Current Insight into Culture-Dependent and Culture-Independent Methods in Discovering Ascomycetous Taxa

Culture techniques are vital in both traditional and modern fungal taxonomy. Establishing sexual-asexual links and synanamorphs, extracting DNA and secondary metabolites are mainly based on cultures. However, it is widely accepted that a large number of species are not sporulating in nature while others cannot be cultured. Recent ecological studies based on culture-independent methods revealed these unculturable taxa, i.e., dark taxa. Recent fungal diversity estimation studies suggested that environmental sequencing plays a vital role in discovering missing species. However, Sanger sequencing is still the main approach in determining DNA sequences in culturable species. In this paper, we summarize culture-based and culture-independent methods in the study of ascomycetous taxa. High-throughput sequencing of leaf endophytes, leaf litter fungi and fungi in aquatic environments is important to determine dark taxa. Nevertheless, currently, naming dark taxa is not recognized by the ICN, thus provisional naming of them is essential as suggested by several studies.

Greater Mouse-Eared Bats (Myotis myotis) Hibernating in the Nietoperek Bat Reserve (Poland) as a Vector of Airborne Culturable Fungi

Bats can contribute to an increase of aeromycota in underground ecosystems and might be a vector/reservoir of microorganisms; however, there is no information about the number and species composition of fungi around hibernating bats. One of the most common species in Europe with direct human contact is the greater mouse-eared bat (Myotis myotis). The goal of our research was the first report of the airborne fungi present in the close vicinity of hibernating M. myotis in the Nietoperek bat reserve (Western Poland) by the use of culture-based techniques and genetic and phenotypic identifications. Aerobiological investigations of mycobiota under hibernating bats were performed on two culture media (PDA and YPG) and at two incubation temperatures (7 and 24 ± 0.5 °C). Overall, we detected 32 fungal species from three phyla (Ascomycota, Basidiomycota, and Zygomycota) and 12 genera. The application of YPG medium and the higher incubation temperature showed higher numbers of isolated fungal species and CFU. Penicillium spp. were dominant in the study, with spores found outside the underground hibernation site from 51.9% to 86.3% and from 56.7% to 100% inside the bat reserve. Penicillium chrysogenum was the most frequently isolated species, then Absidia glauca, Aspergillus fumigatus, A. tubingensis, Mortierella polycephala, Naganishia diffluens, and Rhodotorula mucilaginosa. Temperature, relative humidity, and the abundance of bats correlated positively with the concentration of airborne fungal propagules, between fungal species diversity, and the concentration of aeromycota, but the number of fungal species did not positively correlate with the number of bats. The air in the underground site was more contaminated by fungi than the air outside; however, the concentration of aeromycota does not pose a threat for human health. Nevertheless, hibernating bats contribute to an increase in the aeromycota and as a vector/reservoir of microscopic fungi, including those that may cause allergies and infections in mammals, and should be monitored.

Comparative Study of Metagenomics and Metatranscriptomics to Reveal Microbiomes in Overwintering Pepper Fruits

Red pepper (Capsicum annuum, L.), is one of the most important spice plants in Korea. Overwintering pepper fruits are a reservoir of various microbial pepper diseases. Here, we conducted metagenomics (DNA sequencing) and metatranscriptomics (RNA sequencing) using samples collected from three different fields. We compared two different library types and three different analytical methods for the identification of microbiomes in overwintering pepper fruits. Our results demonstrated that DNA sequencing might be useful for the identification of bacteria and DNA viruses such as bacteriophages, while mRNA sequencing might be beneficial for the identification of fungi and RNA viruses. Among three analytical methods, KRAKEN2 with raw data reads (KRAKEN2_R) might be superior for the identification of microbial species to other analytical methods. However, some microbial species with a low number of reads were wrongly assigned at the species level by KRAKEN2_R. Moreover, we found that the databases for bacteria and viruses were better established as compared to the fungal database with limited genome data. In summary, we carefully suggest that different library types and analytical methods with proper databases should be applied for the purpose of microbiome study.

Advantages and Limitations of 16S rRNA Next-Generation Sequencing for Pathogen Identification in the Diagnostic Microbiology Laboratory: Perspectives from a Middle-Income Country

Bacterial culture and biochemical testing (CBtest) have been the cornerstone of pathogen identification in the diagnostic microbiology laboratory. With the advent of Sanger sequencing and later, next-generation sequencing, 16S rRNA next-generation sequencing (16SNGS) has been proposed to be a plausible platform for this purpose. Nevertheless, usage of the 16SNGS platform has both advantages and limitations. In addition, transition from the traditional methods of CBtest to 16SNGS requires procurement of costly equipment, timely and sustainable maintenance of these platforms, specific facility infrastructure and technical expertise. All these factors pose a challenge for middle-income countries, more so for countries in the lower middle-income range. In this review, we describe the basis for CBtest and 16SNGS, and discuss the limitations, challenges, advantages and future potential of using 16SNGS for bacterial pathogen identification in diagnostic microbiology laboratories of middle-income countries.

Natural Products Repertoire of the Red Sea

Marine natural products have achieved great success as an important source of new lead compounds for drug discovery. The Red Sea provides enormous diversity on the biological scale in all domains of life including micro- and macro-organisms. In this review, which covers the literature to the end of 2019, we summarize the diversity of bioactive secondary metabolites derived from Red Sea micro- and macro-organisms, and discuss their biological potential whenever applicable. Moreover, the diversity of the Red Sea organisms is highlighted as well as their genomic potential. This review is a comprehensive study that compares the natural products recovered from the Red Sea in terms of ecological role and pharmacological activities.

SQMtools: automated processing and visual analysis of 'omics data with R and anvi'o

BACKGROUND

The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches.

RESULTS

Here we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi'o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment.

CONCLUSIONS

Altogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands.

Targeted domain assembly for fast functional profiling of metagenomic datasets with S3A

Motivation

The understanding of the ever-increasing number of metagenomic sequences accumulating in our databases demands for approaches that rapidly ‘explore’ the content of multiple and/or large metagenomic datasets with respect to specific domain targets, avoiding full domain annotation and full assembly.

Results

S3A is a fast and accurate domain-targeted assembler designed for a rapid functional profiling. It is based on a novel construction and a fast traversal of the Overlap-Layout-Consensus graph, designed to reconstruct coding regions from domain annotated metagenomic sequence reads. S3A relies on high-quality domain annotation to efficiently assemble metagenomic sequences and on the design of a new confidence measure for a fast evaluation of overlapping reads. Its implementation is highly generic and can be applied to any arbitrary type of annotation. On simulated data, S3A achieves a level of accuracy similar to that of classical metagenomics assembly tools while permitting to conduct a faster and sensitive profiling on domains of interest. When studying a few dozens of functional domains—a typical scenario—S3A is up to an order of magnitude faster than general purpose metagenomic assemblers, thus enabling the analysis of a larger number of datasets in the same amount of time. S3A opens new avenues to the fast exploration of the rapidly increasing number of metagenomic datasets displaying an ever-increasing size.

Availability and implementation

S3A is available at http://www.lcqb.upmc.fr/S3A_ASSEMBLER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparison of microbiome samples: methods and computational challenges

The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage

Motivation

Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.

Results

In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2904-4) contains supplementary material, which is available to authorized users.

Human microbial ecology and the rising new medicine

The first life forms on earth were Prokaryotic, and the evolution of all Eukaryotic life occurred with the help of bacteria. Animal-associated microbiota also includes members of the archaea, fungi, protists, and viruses. The genomes of this host-associated microbial life are called the microbiome. Across the mammalian tree, microbiomes guarantee the development of immunity, physiology, and resistance to pathogens. In humans, all surfaces and cavities are colonized by a microbiome, maintained by a careful balance between the host response and its colonizers-thus humans are considered now supraorganisms. These microbiomes supply essential ecosystem services that benefit health through homeostasis, and the loss of the indigenous microbiota leads to dysbiosis, which can have significant consequences to disease. This educational review aims to describe the importance of human microbial ecology, explain the ecological terms applied to the study of the human microbiome, developments within the cutting-edge microbiome field, and implications to diagnostic and treatment.

Metagenomics analysis of cocoa bean fermentation microbiome identifying species diversity and putative functional capabilities

Fermentation of Theobroma cacao L. beans is the most critical stage in the production of cocoa products such as chocolates and its derivatives. There is a limited understanding of the complex response of microbial diversity during cocoa bean fermentation. The aim of the present study was to investigate microbial communities in the cocoa bean fermentation heap using a culture-independent approach to elucidate microbial diversity, structure, functional annotation and mapping unto metabolic pathways. Genomic DNA was extracted and purified from a sample of cocoa beans fermentation heap and was followed by library preparations. Sequence data was generated on Illumina Hiseq 2000 paired-end technology (Macrogen Inc). Taxonomic analysis based on genes predicted from the metagenome identified a high percentage of Bacteria (90.0%), Yeast (9%), and bacteriophages (1%) from the cocoa microbiome. Lactobacillus (20%), Gluconacetobacter (9%), Acetobacter (7%) and Gluconobacter (6%) dominated this study. The mean species diversity, measured by Shannon alpha-diversity index, was estimated at 142.81. Assignment of metagenomic sequences to SEED database categories at 97% sequence similarity identified a genetic profile characteristic of heterotrophic lactic acid fermentation of carbohydrates and aromatic amino acids. Metabolism of aromatic compounds, amino acids and their derivatives and carbohydrates occupied 0.6%, 8% and 13% respectively. Overall, these results provide insights into the cocoa microbiome, identifying fermentation processes carried out broadly by complex microbial communities and metabolic pathways encoding aromatic compounds such as phenylacetaldehyde, butanediol, acetoin, and theobromine that are required for flavour and aroma production. The results obtained will help develop targeted inoculations to produce desired chocolate flavour or targeted metabolic pathways for the selection of microbes for good aroma and flavour compounds formation.

Shotgun metagenomic sequencing from Manao-Pee cave, Thailand, reveals insight into the microbial community structure and its metabolic potential

Background

Due to the cave oligotrophic environment, this habitat presents a challenge for microorganisms to colonize and thrive. However, it has been well documented that microorganisms play important roles in cave development. Survival of microbes in this unique habitat likely involves a broad range of adaptive capabilities. Recently, cave microbiomes all over the world are of great scientific interest. However, the majority of investigations focused mostly on small subunit ribosomal RNA (16S rRNA) gene, leaving the ecological role of the microbial community largely unknown. Here, we are particularly interested in exploring the taxonomic composition and metabolic potential of microorganisms in soil from Manao-Pee cave, a subterranean limestone cave in the western part of Thailand, by using high-throughput shotgun metagenomic sequencing.

Results

From taxonomic composition analysis using ribosomal RNA genes (rRNA), the results confirmed that Actinobacteria (51.2%) and Gammaproteobacteria (24.4%) were the dominant bacterial groups in the cave soil community. Metabolic potential analysis, based on six functional modules of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, revealed that functional genes involved in microbial metabolisms are highly represented in this community (40.6%). To better understand how microbes thrive under unfavorable cave condition, we focused on microbial energy metabolism. The results showed that microbial genes involved in oxidative phosphorylation were the most dominant (28.8%) in Manao-Pee cave, and were followed by methane metabolism (20.5%), carbon fixation (16.0%), nitrogen metabolism (14.7%), and sulfur metabolism (6.3%). In addition, microbial genes involved in xenobiotic biodegradation (26 pathways) and in production of secondary metabolites (27 pathways) were also identified.

Conclusion

In addition to providing information on microbial diversity, we also gained insights into microbial adaptations and survival strategies under cave conditions. Based on rRNA genes, the results revealed that bacteria belonging to the Actinobacteria and Gammaproteobacteria were the most abundant in this community. From metabolic potential analysis, energy and nutrient sources that sustain diverse microbial population in this community might be atmospheric gases (methane, carbon dioxide, nitrogen), inorganic sulfur, and xenobiotic compounds. In addition, the presence of biosynthetic pathways of secondary metabolites suggested that they might play important ecological roles in the cave microbiome.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1521-8) contains supplementary material, which is available to authorized users.

Intestinal microbiome and autoimmune liver disease

At present, it has been proved that intestinal microbial-related disorders are involved in the development and progression of multi-organ system diseases. Intestinal microflora is the accumulation of microbial antigens and activated immune cells. Changes in the composition of intestinal microflora (biological disorders) can destroy the systemic immune tolerance of intestinal and symbiotic bacteria. Toll-like receptors in the intestine recognize microbial-related molecular patterns and T helper lymphocyte subpopulations that can cross-react with host antigens (molecular mimics). Activated enterogenous lymphocytes can migrate to lymph nodes, and enterogenous microbial antigens can migrate to extraintestinal sites. Inflammasomes can form in hepatocytes and hepatic stellate cells, which can drive inflammatory, immune-mediated and fibrotic responses. This article reviews and evaluates the role of intestinal microorganisms in the pathogenesis and treatment of autoimmune liver disease.

Metabarcoding by capture using a single COI probe (MCSP) to identify and quantify fish species in ichthyoplankton swarms

The ability to determine the composition and relative frequencies of fish species in large ichthyoplankton swarms could have extremely important ecological applications However, this task is currently hampered by methodological limitations. We proposed a new method for Amazonian species based on hybridization capture of the COI gene DNA from a distant species (Danio rerio), absent from our study area (the Amazon basin). The COI sequence of this species is approximately equidistant from all COI of Amazonian species available. By using this sequence as probe we successfully facilitated the simultaneous identification of fish larvae belonging to the order Siluriformes and to the Characiformes represented in our ichthyoplankton samples. Species relative frequencies, estimated by the number of reads, showed almost perfect correlations with true frequencies estimated by a Sanger approach, allowing the development of a quantitative approach. We also proposed a further improvement to a previous protocol, which enables lowering the sequencing effort by 40 times. This new Metabarcoding by Capture using a Single Probe (MCSP) methodology could have important implications for ecology, fisheries management and conservation in fish biodiversity hotspots worldwide. Our approach could easily be extended to other plant and animal taxa.

Lactobacillus plantarum-derived Extracellular Vesicles Protect Atopic Dermatitis Induced by Staphylococcus aureus-derived Extracellular Vesicles

PURPOSE

The microbial environment is an important factor that contributes to the pathogenesis of atopic dermatitis (AD). Recently, it was revealed that not only bacteria itself but also extracellular vesicles (EVs) secreted from bacteria affect the allergic inflammation process. However, almost all research carried out so far was related to local microorganisms, not the systemic microbial distribution. We aimed to compare the bacterial EV composition between AD patients and healthy subjects and to experimentally find out the beneficial effect of some bacterial EV composition.

METHODS

Twenty-seven AD patients and 6 healthy control subjects were enrolled. After urine and serum were obtained, EVs were prepared from samples. Metagenomic analysis of 16s ribosomal DNA extracted from the EVs was performed, and bacteria showing the greatest difference between controls and patients were identified. In vitro and in vivo therapeutic effects of significant bacterial EV were evaluated with keratinocytes and with Staphylococcus aureus-induced mouse AD models, respectively.

RESULTS

The proportions of Lactococcus, Leuconostoc and Lactobacillus EVs were significantly higher and those of Alicyclobacillus and Propionibacterium were lower in the control group than in the AD patient group. Therefore, lactic acid bacteria were considered to be important ones that contribute to the difference between the patient and control groups. In vitro, interleukin (IL)-6 from keratinocytes and macrophages decreased and cell viability was restored with Lactobacillus plantarum-derived EV treatment prior to S. aureus EV treatment. In S. aureus-induced mouse AD models, L. plantarum-derived EV administration reduced epidermal thickening and the IL-4 level.

CONCLUSIONS

We suggested the protective role of lactic acid bacteria in AD based on metagenomic analysis. Experimental findings further suggest that L. plantarum-derived EV could help prevent skin inflammation.

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling

BACKGROUND

Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.

RESULTS

MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.

CONCLUSIONS

Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

Removing contaminants from databases of draft genomes

Metagenomic sequencing of patient samples is a very promising method for the diagnosis of human infections. Sequencing has the ability to capture all the DNA or RNA from pathogenic organisms in a human sample. However, complete and accurate characterization of the sequence, including identification of any pathogens, depends on the availability and quality of genomes for comparison. Thousands of genomes are now available, and as these numbers grow, the power of metagenomic sequencing for diagnosis should increase. However, recent studies have exposed the presence of contamination in published genomes, which when used for diagnosis increases the risk of falsely identifying the wrong pathogen. To address this problem, we have developed a bioinformatics system for eliminating contamination as well as low-complexity genomic sequences in the draft genomes of eukaryotic pathogens. We applied this software to identify and remove human, bacterial, archaeal, and viral sequences present in a comprehensive database of all sequenced eukaryotic pathogen genomes. We also removed low-complexity genomic sequences, another source of false positives. Using this pipeline, we have produced a database of “clean” eukaryotic pathogen genomes for use with bioinformatics classification and analysis tools. We demonstrate that when attempting to find eukaryotic pathogens in metagenomic samples, the new database provides better sensitivity than one using the original genomes while offering a dramatic reduction in false positives.

Infectious diseases afflict a majority of the human population around the world, from the common cold to the devastating malaria parasite. As technology has evolved, DNA sequencing emerged as a revolutionary and rapid method for diagnosing human infections. As part of our efforts to boost the ability of scientists to identify the source of an infection by sequencing, we present here a computational method for removing erroneous or misleading sequences from existing DNA databases. When we applied this method to a database of more than 200 eukaryotic pathogens, we were able to successfully and accurately identify the true pathogens infecting real human samples.

Reef-Building Corals as a Tool for Climate Change Research in the Genomics Era

Coral reef ecosystems are among the most biodiverse habitats in the marine realm. They not only contribute with a plethora of ecosystem services, but they also are beneficial to humankind via nurturing marine fisheries and sustaining recreational activities. We will discuss the biology of coral reefs and their ecophysiology including the complex bacterial microbiota associated with them.

The skin microbiome and immune system: Potential target for chemoprevention?

There has been increasing interest in understanding the role of the human microbiome in skin diseases. Microbiome studies are being utilized in skin cancer research in numerous ways. Commensal bacteria are being studied as a potential tool to judge the biggest environmental risk of skin cancer, ultraviolet (UV) radiation. Owing to the recognized link of skin microbes in the process of inflammation, there have been theories linking commensal bacteria to skin cancer. Viral metagenomics has also provided insight into virus linked forms of skin cancers. Speculations can be drawn for skin microbiome that in a manner similar to gut microbiome, they can be involved in chemoprevention of skin cancer. Nonetheless, there are definitely huge gaps in our knowledge of the relationship of microbiome and skin cancers, especially in relation to chemoprevention. The utilization of microbiome in skin cancer research seems to be a promising field and may help yield novel skin cancer prevention and treatment options. This review focuses on recent utilization of the microbiome in skin cancer research, and it explores the potential of utilizing the microbiome in prevention, earlier diagnosis, and treatment of skin cancers.

Unsupervised Binning of Metagenomic Assembled Contigs Using Improved Fuzzy C-Means Method

Metagenomic contigs binning is a necessary step of metagenome analysis. After assembly, the number of contigs belonging to different genomes is usually unequal. So a metagenomic contigs dataset is a kind of imbalanced dataset and traditional fuzzy c-means method (FCM) fails to handle it very well. In this paper, we will introduce an improved version of fuzzy c-means method (IFCM) into metagenomic contigs binning. First, tetranucleotide frequencies are calculated for every contig. Second, the number of bins is roughly estimated by the distribution of genome lengths of a complete set of non-draft sequenced microbial genomes from NCBI. Then, IFCM is used to cluster DNA contigs with the estimated result. Finally, a clustering validity function is utilized to determine the binning result. We tested this method on a synthetic and two real datasets and experimental results have showed the effectiveness of this method compared with other tools.

How to normalize metatranscriptomic count data for differential expression analysis

Background

Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics.

Methods

We propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data under this model requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows us to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script.

Results

When applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data with an organism-independent (global) scaling of counts the resulting differences may be difficult to interpret. The differences may correspond to changing functional profiles of the contributing organisms but may also result from a variation of taxonomic abundances. Taxon-specific scaling eliminates this variation and therefore the resulting differences actually reflect a different behavior of organisms under changing conditions. In simulation studies we show that the divergence between results from global and taxon-specific scaling can be drastic. In particular, the variation of organism abundances can imply a considerable increase of significant differences with global scaling. Also, on real metatranscriptomic data, the predictions from taxon-specific and global scaling can differ widely. Our studies indicate that in real data applications performed with global scaling it might be impossible to distinguish between differential expression in terms of transcriptomic changes and differential composition in terms of changing taxonomic proportions.

Conclusions

As in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore provides a clear interpretation of the observed functional differences.

Ecosystem biomonitoring with eDNA: metabarcoding across the tree of life in a tropical marine environment

Effective marine management requires comprehensive data on the status of marine biodiversity. However, efficient methods that can document biodiversity in our oceans are currently lacking. Environmental DNA (eDNA) sourced from seawater offers a new avenue for investigating the biota in marine ecosystems. Here, we investigated the potential of eDNA to inform on the breadth of biodiversity present in a tropical marine environment. Directly sequencing eDNA from seawater using a shotgun approach resulted in only 0.34% of 22.3 million reads assigning to eukaryotes, highlighting the inefficiency of this method for assessing eukaryotic diversity. In contrast, using 'tree of life' (ToL) metabarcoding and 20-fold fewer sequencing reads, we could detect 287 families across the major divisions of eukaryotes. Our data also show that the best performing 'universal' PCR assay recovered only 44% of the eukaryotes identified across all assays, highlighting the need for multiple metabarcoding assays to catalogue biodiversity. Lastly, focusing on the fish genus Lethrinus, we recovered intra- and inter-specific haplotypes from seawater samples, illustrating that eDNA can be used to explore diversity beyond taxon identifications. Given the sensitivity and low cost of eDNA metabarcoding we advocate this approach be rapidly integrated into biomonitoring programs.

DMclust, a Density-based Modularity Method for Accurate OTU Picking of 16S rRNA Sequences

Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is a crucial step in analyzing metagenomic data. Although many methods have been developed, how to obtain an appropriate balance between clustering accuracy and computational efficiency is still a major challenge. A novel density-based modularity clustering method, called DMclust, is proposed in this paper to bin 16S rRNA sequences into OTUs with high clustering accuracy. The DMclust algorithm consists of four main phases. It first searches for the sequence dense group defined as n-sequence community, in which the distance between any two sequences is less than a threshold. Then these dense groups are used to construct a weighted network, where dense groups are viewed as nodes, each pair of dense groups is connected by an edge, and the distance of pairwise groups represents the weight of the edge. Then, a modularity-based community detection method is employed to generate the preclusters. Finally, the remaining sequences are assigned to their nearest preclusters to form OTUs. Compared with existing widely used methods, the experimental results on several metagenomic datasets show that DMclust has higher accurate clustering performance with acceptable memory usage.

Machine learning for metagenomics: methods and tools

Owing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.

Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: A review

Bioaerosols significantly affect atmospheric processes while they undergo long-range vertical and horizontal transport and influence atmospheric chemistry and physics and climate change. Accumulating evidence suggests that exposure to bioaerosols may cause adverse health effects, including severe disease. Studies of bioaerosols have primarily focused on their chemical composition and largely neglected their biological composition and the negative effects of biological composition on ecosystems and human health. Here, current molecular methods for the identification, quantification, and distribution of bioaerosol agents are reviewed. Modern developments in environmental microbiology technology would be favorable in elucidation of microbial temporal and spatial distribution in the atmosphere at high resolution. In addition, these provide additional supports for growing evidence that microbial diversity or composition in the bioaerosol is an indispensable environmental aspect linking with public health.

Factoring the intestinal microbiome into the pathogenesis of autoimmune hepatitis

The intestinal microbiome is a reservoir of microbial antigens and activated immune cells. The aims of this review were to describe the role of the intestinal microbiome in generating innate and adaptive immune responses, indicate how these responses contribute to the development of systemic immune-mediated diseases, and encourage investigations that improve the understanding and management of autoimmune hepatitis. Alterations in the composition of the intestinal microflora (dysbiosis) can disrupt intestinal and systemic immune tolerances for commensal bacteria. Toll-like receptors within the intestine can recognize microbe-associated molecular patterns and shape subsets of T helper lymphocytes that may cross-react with host antigens (molecular mimicry). Activated gut-derived lymphocytes can migrate to lymph nodes, and gut-derived microbial antigens can translocate to extra-intestinal sites. Inflammasomes can form within hepatocytes and hepatic stellate cells, and they can drive the pro-inflammatory, immune-mediated, and fibrotic responses. Diet, designer probiotics, vitamin supplements, re-colonization methods, antibiotics, drugs that decrease intestinal permeability, and molecular interventions that block signaling pathways may emerge as adjunctive regimens that complement conventional immunosuppressive management. In conclusion, investigations of the intestinal microbiome are warranted in autoimmune hepatitis and promise to clarify pathogenic mechanisms and suggest alternative management strategies.

Metagenomics: Probing pollutant fate in natural and engineered ecosystems

Polluted environments are a reservoir of microbial species able to degrade or to convert pollutants to harmless compounds. The proper management of microbial resources requires a comprehensive characterization of their genetic pool to assess the fate of contaminants and increase the efficiency of bioremediation processes. Metagenomics offers appropriate tools to describe microbial communities in their whole complexity without lab-based cultivation of individual strains. After a decade of use of metagenomics to study microbiomes, the scientific community has made significant progress in this field. In this review, we survey the main steps of metagenomics applied to environments contaminated with organic compounds or heavy metals. We emphasize technical solutions proposed to overcome encountered obstacles. We then compare two metagenomic approaches, i.e. library-based targeted metagenomics and direct sequencing of metagenomes. In the former, environmental DNA is cloned inside a host, and then clones of interest are selected based on (i) their expression of biodegradative functions or (ii) sequence homology with probes and primers designed from relevant, already known sequences. The highest score for the discovery of novel genes and degradation pathways has been achieved so far by functional screening of large clone libraries. On the other hand, direct sequencing of metagenomes without a cloning step has been more often applied to polluted environments for characterization of the taxonomic and functional composition of microbial communities and their dynamics. In this case, the analysis has focused on 16S rRNA genes and marker genes of biodegradation. Advances in next generation sequencing and in bioinformatic analysis of sequencing data have opened up new opportunities for assessing the potential of biodegradation by microbes, but annotation of collected genes is still hampered by a limited number of available reference sequences in databases. Although metagenomics is still facing technical and computational challenges, our review of the recent literature highlights its value as an aid to efficiently monitor the clean-up of contaminated environments and develop successful strategies to mitigate the impact of pollutants on ecosystems.

Testing the Neutral Theory of Biodiversity with Human Microbiome Datasets

The human microbiome project (HMP) has made it possible to test important ecological theories for arguably the most important ecosystem to human health-the human microbiome. Existing limited number of studies have reported conflicting evidence in the case of the neutral theory; the present study aims to comprehensively test the neutral theory with extensive HMP datasets covering all five major body sites inhabited by the human microbiome. Utilizing 7437 datasets of bacterial community samples, we discovered that only 49 communities (less than 1%) satisfied the neutral theory, and concluded that human microbial communities are not neutral in general. The 49 positive cases, although only a tiny minority, do demonstrate the existence of neutral processes. We realize that the traditional doctrine of microbial biogeography "Everything is everywhere, but the environment selects" first proposed by Baas-Becking resolves the apparent contradiction. The first part of Baas-Becking doctrine states that microbes are not dispersal-limited and therefore are neutral prone, and the second part reiterates that the freely dispersed microbes must endure selection by the environment. Therefore, in most cases, it is the host environment that ultimately shapes the community assembly and tip the human microbiome to niche regime.

MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects

Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species in environmental samples. In this study, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains). Different from all existing methods, MBMC bins reads by measuring the similarity of reads to the trained Markov chains for different taxa instead of directly comparing reads with known genomic sequences. By testing on more than 24 simulated and experimental datasets with species of similar abundance, species of low abundance, and/or unknown species, we report here that MBMC reliably grouped reads from different species into separate bins. Compared with four existing approaches, we demonstrated that the performance of MBMC was comparable with existing approaches when binning reads from sequenced species, and superior to existing approaches when binning reads from unknown species. MBMC is a pivotal tool for binning metagenomic reads in the current era of Big Data and postgenomic integrative biology. The MBMC software can be freely downloaded at http://hulab.ucf.edu/research/projects/metagenomics/MBMC.html .