Toward Accurate and Quantitative Comparative Metagenomics.

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized.

Comparison of the performance of two targeted metagenomic virus capture probe-based methods using reference control materials and clinical samples.

Viral enrichment by probe hybridization has been reported to significantly increase the sensitivity of viral metagenomics. This study compares the analytical performance of two targeted metagenomic virus capture probe-based methods: (i) SeqCap EZ HyperCap by Roche (ViroCap) and (ii) Twist Comprehensive Viral Research Panel workflow, for diagnostic use. Sensitivity, specificity, and limit of detection were analyzed using 25 synthetic viral sequences spiked in increasing proportions of human background DNA, eight clinical samples, and American Type Culture Collection (ATCC) Virome Virus Mix. Sensitivity and specificity were 95% and higher for both methods using the synthetic and reference controls as gold standard. Combining thresholds for viral sequence read counts and genome coverage [respectively 500 reads per million (RPM) and 10% coverage] resulted in optimal prediction of true positive results. Limits of detection were approximately 50-500 copies/mL for both methods as determined by ddPCR. Increasing proportions of spike-in cell-free human background sequences up to 99.999% (50 ng/mL) did not negatively affect viral detection, suggesting effective capture of viral sequences. These data show analytical performances in ranges applicable to clinical samples, for both probe hybridization metagenomic approaches. This study supports further steps toward more widespread use of viral metagenomics for pathogen detection, in clinical and surveillance settings using low biomass samples. IMPORTANCE: Viral metagenomics has been gradually applied for broad-spectrum pathogen detection of infectious diseases, surveillance of emerging diseases, and pathogen discovery. Viral enrichment by probe hybridization methods has been reported to significantly increase the sensitivity of viral metagenomics. During the past years, a specific hybridization panel distributed by Roche has been adopted in a broad range of different clinical and zoonotic settings. Recently, Twist Bioscience has released a new hybridization panel targeting human and animal viruses. This is the first report comparing the performance of viral metagenomic hybridization panels.

Benchmarking of virome metagenomic analysis approaches using a large, 60+ members, viral synthetic community.

IMPORTANCE: We report here efforts to benchmark performance of two widespread approaches for virome analysis, which target either virion-associated nucleic acids (VANA) or highly purified double-stranded RNAs (dsRNAs). This was achieved using synthetic communities of varying complexity levels, up to a highly complex community of 72 viral agents (115 viral molecules) comprising isolates from 21 families and 61 genera of plant viruses. The results obtained confirm that the dsRNA-based approach provides a more complete representation of the RNA virome, in particular, for high complexity ones. However, for viromes of low to medium complexity, VANA appears a reasonable alternative and would be the preferred choice if analysis of DNA viruses is of importance. Several parameters impacting performance were identified as well as a direct relationship between the completeness of virome description and sample sequencing depth. The strategy, results, and tools used here should prove useful in a range of virome analysis efforts.

How accurately can we assess zoonotic risk?

Identifying the animal reservoirs from which zoonotic viruses will likely emerge is central to understanding the determinants of disease emergence. Accordingly, there has been an increase in studies attempting zoonotic "risk assessment." Herein, we demonstrate that the virological data on which these analyses are conducted are incomplete, biased, and rapidly changing with ongoing virus discovery. Together, these shortcomings suggest that attempts to assess zoonotic risk using available virological data are likely to be inaccurate and largely only identify those host taxa that have been studied most extensively. We suggest that virus surveillance at the human-animal interface may be more productive.

Meta-Prism: Ultra-fast and highly accurate microbial community structure search utilizing dual indexing and parallel computation.

Microbiome samples are accumulating at an unprecedented speed. As a result, a massive amount of samples have become available for the mining of the intrinsic patterns among them. However, due to the lack of advanced computational tools, fast yet accurate comparisons and searches among thousands to millions of samples are still in urgent need. In this work, we proposed the Meta-Prism method for comparing and searching the microbial community structures amongst tens of thousands of samples. Meta-Prism is at least 10 times faster than contemporary methods serving the same purpose and can provide very accurate search results. The method is based on three computational techniques: dual-indexing approach for sample subgrouping, refined scoring function that could scrutinize the minute differences among samples, and parallel computation on CPU or GPU. The superiority of Meta-Prism on speed and accuracy for multiple sample searches is proven based on searching against ten thousand samples derived from both human and environments. Therefore, Meta-Prism could facilitate similarity search and in-depth understanding among massive number of heterogenous samples in the microbiome universe. The codes of Meta-Prism are available at: https://github.com/HUST-NingKang-Lab/metaPrism.

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.

Current challenges and best-practice protocols for microbiome analysis.

Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

Culture-free genome-wide locus sequence typing (GLST) provides new perspectives on Trypanosoma cruzi dispersal and infection complexity.

Analysis of genetic polymorphism is a powerful tool for epidemiological surveillance and research. Powerful inference from pathogen genetic variation, however, is often restrained by limited access to representative target DNA, especially in the study of obligate parasitic species for which ex vivo culture is resource-intensive or bias-prone. Modern sequence capture methods enable pathogen genetic variation to be analyzed directly from host/vector material but are often too complex and expensive for resource-poor settings where infectious diseases prevail. This study proposes a simple, cost-effective 'genome-wide locus sequence typing' (GLST) tool based on massive parallel amplification of information hotspots throughout the target pathogen genome. The multiplexed polymerase chain reaction amplifies hundreds of different, user-defined genetic targets in a single reaction tube, and subsequent agarose gel-based clean-up and barcoding completes library preparation at under 4 USD per sample. Our study generates a flexible GLST primer panel design workflow for Trypanosoma cruzi, the parasitic agent of Chagas disease. We successfully apply our 203-target GLST panel to direct, culture-free metagenomic extracts from triatomine vectors containing a minimum of 3.69 pg/µl T. cruzi DNA and further elaborate on method performance by sequencing GLST libraries from T. cruzi reference clones representing discrete typing units (DTUs) TcI, TcIII, TcIV, TcV and TcVI. The 780 SNP sites we identify in the sample set repeatably distinguish parasites infecting sympatric vectors and detect correlations between genetic and geographic distances at regional (< 150 km) as well as continental scales. The markers also clearly separate TcI, TcIII, TcIV and TcV + TcVI and appear to distinguish multiclonal infections within TcI. We discuss the advantages, limitations and prospects of our method across a spectrum of epidemiological research.

Comparison of targeted metagenomics and IS-Pro methods for analysing the lung microbiome.

BACKGROUND: Targeted metagenomics and IS-Pro method are two of the many methods that have been used to study the microbiome. The two methods target different regions of the 16 S rRNA gene. The aim of this study was to compare targeted metagenomics and IS-Pro methods for the ability to discern the microbial composition of the lung microbiome of COPD patients. METHODS: Spontaneously expectorated sputum specimens were collected from COPD patients. Bacterial DNA was extracted and used for targeted metagenomics and IS-Pro method. The analysis was performed using QIIME2 (targeted metagenomics) and IS-Pro software (IS-Pro method). Additionally, a laboratory cost per isolate and time analysis was performed for each method. RESULTS: Statistically significant differences were observed in alpha diversity when targeted metagenomics and IS-Pro methods' data were compared using the Shannon diversity measure (p-value = 0.0006) but not with the Simpson diversity measure (p-value = 0.84). Distinct clusters with no overlap between the two technologies were observed for beta diversity. Targeted metagenomics had a lower relative abundance of phyla, such as the Proteobacteria, and higher relative abundance of phyla, such as Firmicutes when compared to the IS-Pro method. Haemophilus, Prevotella and Streptococcus were most prevalent genera across both methods. Targeted metagenomics classified 23 % (144/631) of OTUs to a species level, whereas IS-Pro method classified 86 % (55/64) of OTUs to a species level. However, unclassified OTUs accounted for a higher relative abundance when using the IS-Pro method (35 %) compared to targeted metagenomics (5 %). The two methods performed comparably in terms of cost and time; however, the IS-Pro method was more user-friendly. CONCLUSIONS: It is essential to understand the value of different methods for characterisation of the microbiome. Targeted metagenomics and IS-Pro methods showed differences in ability in identifying and characterising OTUs, diversity and microbial composition of the lung microbiome. The IS-Pro method might miss relevant species and could inflate the abundance of Proteobacteria. However, the IS-Pro kit identified most of the important lung pathogens, such as Burkholderia and Pseudomonas and may work in a more diagnostics-orientated setting. Both methods were comparable in terms of cost and time; however, the IS-Pro method was easier to use.

16S rRNA Gene Copy Number Normalization Does Not Provide More Reliable Conclusions in Metataxonomic Surveys.

Sequencing 16S rRNA gene amplicons is the gold standard to uncover the composition of prokaryotic communities. The presence of multiple copies of this gene makes the community abundance data distorted and gene copy normalization (GCN) necessary for correction. Even though GCN of 16S data provided a picture closer to the metagenome before, it should also be compared with communities of known composition due to the fact that library preparation is prone to methodological biases. Here, we process 16S rRNA gene amplicon data from eleven simple mock communities with DADA2 and estimate the impact of GCN. In all cases, the mock community composition derived from the 16S sequencing differs from those expected, and GCN fails to improve the classification for most of the analysed communities. Our approach provides empirical evidence that GCN does not improve the 16S target sequencing analyses in real scenarios. We therefore question the use of GCN for metataxonomic surveys until a more comprehensive catalogue of copy numbers becomes available.

gcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data.

Meta-omics approaches have been increasingly used to study the structure and function of the microbial communities. A variety of large-scale collaborative projects are being conducted to encompass samples from diverse environments and habitats. This change has resulted in enormous demands for long-term data maintenance and capacity for data analysis. The Global Catalogue of Metagenomics (gcMeta) is a part of the 'Chinese Academy of Sciences Initiative of Microbiome (CAS-CMI)', which focuses on studying the human and environmental microbiome, establishing depositories of samples, strains and data, as well as promoting international collaboration. To accommodate and rationally organize massive datasets derived from several thousands of human and environmental microbiome samples, gcMeta features a database management system for archiving and publishing data in a standardized way. Another main feature is the integration of more than ninety web-based data analysis tools and workflows through a Docker platform which enables data analysis by using various operating systems. This platform has been rapidly expanding, and now hosts data from the CAS-CMI and a number of other ongoing research projects. In conclusion, this platform presents a powerful and user-friendly service to support worldwide collaborative efforts in the field of meta-omics research. This platform is freely accessible at https://gcmeta.wdcm.org/.

Improving the usability and comprehensiveness of microbial databases.

Metagenomics studies leverage genomic reference databases to generate discoveries in basic science and translational research. However, current microbial studies use disparate reference databases that lack consistent standards of specimen inclusion, data preparation, taxon labelling and accessibility, hindering their quality and comprehensiveness, and calling for the establishment of recommendations for reference genome database assembly. Here, we analyze existing fungal and bacterial databases and discuss guidelines for the development of a master reference database that promises to improve the quality and quantity of omics research.

Do the human gut metagenomic species possess the minimal set of core functionalities necessary for life?

BACKGROUND: Advances in bioinformatics recently allowed for the recovery of 'metagenomes assembled genomes' from human microbiome studies carried on with shotgun sequencing techniques. Such approach is used as a mean to discover new unclassified metagenomic species, putative biological entities having distinct metabolic traits. RESULTS: In the present analysis we compare 400 genomes from isolates available on NCBI database and 10,000 human gut metagenomic species, screening all of them for the presence of a minimal set of core functionalities necessary, but not sufficient, for life. As a result, the metagenome-assembled genomes resulted systematically depleted in genes encoding for essential functions apparently needed to support autonomous bacterial life. CONCLUSIONS: The relevant degree of lacking core functionalities that we observed in metagenome-assembled genomes raises some concerns about the effective completeness of metagenome-assembled genomes, suggesting caution in extrapolating biological information about their metabolic propensity and ecology in a complex environment like the human gastrointestinal tract.

Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics.

Introduction: Metaproteomics is an established method to obtain a comprehensive taxonomic and functional view of microbial communities. After more than a decade, we are now able to describe the promise, reality, and perspectives of metaproteomics and provide useful information about the choice of method, applications, and potential improvement strategies.Areas covered: In this article, we will discuss current challenges of species and proteome coverage, and also highlight functional aspects of metaproteomics analysis of microbial communities with different levels of complexity. To do this, we re-analyzed data from microbial communities with low to high complexity (8, 72, 200 and >300 species). High species diversity leads to a reduced number of protein group identifications in a complex community, and thus the number of species resolved is underestimated. Ultimately, low abundance species remain undiscovered in complex communities. However, we observed that the main functional categories were better represented within complex microbiomes when compared to species coverage.Expert opinion: Our findings showed that even with low species coverage, metaproteomics has the potential to reveal habitat-specific functional features. Finally, we exploit this information to highlight future research avenues that are urgently needed to enhance our understanding of taxonomic composition and functions of complex microbiomes.

Navigating Clinical Utilization of Direct-from-Specimen Metagenomic Pathogen Detection: Clinical Applications, Limitations, and Testing Recommendations.

BACKGROUND: Metagenomic next generation sequencing (mNGS) is becoming increasingly available for pathogen detection directly from clinical specimens. These tests use target-independent, shotgun sequencing to detect potentially unlimited organisms. The promise of this methodology to aid infection diagnosis is demonstrated through early case reports and clinical studies. However, the optimal role of mNGS in clinical microbiology remains uncertain. CONTENT: We reviewed studies reporting clinical use of mNGS for pathogen detection from various specimen types, including cerebrospinal fluid, plasma, lower respiratory specimens, and others. Published clinical study data were critically evaluated and summarized to identify promising clinical indications for mNGS-based testing, to assess the clinical impact of mNGS for each indication, and to recognize test limitations. Based on these clinical studies, early testing recommendations are made to guide clinical utilization of mNGS for pathogen detection. Finally, current barriers to routine clinical laboratory implementation of mNGS tests are highlighted. SUMMARY: The promise of direct-from-specimen mNGS to enable challenging infection diagnoses has been demonstrated through early clinical studies of patients with meningitis or encephalitis, invasive fungal infections, community acquired pneumonia, and other clinical indications. However, the proportion of patient cases with positive clinical impact due to mNGS testing is low in published studies and the cost of testing is high, emphasizing the importance of improving our understanding of 'when to test' and for which patients mNGS testing is appropriate.

Microbiome Diagnostics.

BACKGROUND: During the past decade, breakthroughs in sequencing technology and computational biology have provided the basis for studies of the myriad ways in which microbial communities ("microbiota") in and on the human body influence human health and disease. In almost every medical specialty, there is now a growing interest in accurate and replicable profiling of the microbiota for use in diagnostic and therapeutic application. CONTENT: This review provides an overview of approaches, challenges, and considerations for diagnostic applications borrowing from other areas of molecular diagnostics, including clinical metagenomics. Methodological considerations and evolving approaches for microbiota profiling from mitochondrially encoded 16S rRNA-based amplicon sequencing to metagenomics and metatranscriptomics are discussed. To improve replicability, at least the most vulnerable steps in testing workflows will need to be standardized and continuous efforts needed to define QC standards. Challenges such as purity of reagents and consumables, improvement of reference databases, and availability of diagnostic-grade data analysis solutions will require joint efforts across disciplines and with manufacturers. SUMMARY: The body of literature supporting important links between the microbiota at different anatomic sites with human health and disease is expanding rapidly and therapeutic manipulation of the intestinal microbiota is becoming routine. The next decade will likely see implementation of microbiome diagnostics in diagnostic laboratories to fully capitalize on technological and scientific advances and apply them in routine medical practice.

A machine learning-based service for estimating quality of genomes using PATRIC.

BACKGROUND: Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and current metagenomic technology supports assemblies of many draft-quality genomes from a single sample, most of which will be novel. DESCRIPTION: We have added two quality assessment tools to the PATRIC annotation pipeline. EvalCon uses supervised machine learning to calculate an annotation consistency score. EvalG implements a variant of the CheckM algorithm to estimate contamination and completeness of an annotated genome.We report on the performance of these tools and the potential utility of the consistency score. Additionally, we provide contamination, completeness, and consistency measures for all genomes in PATRIC and in a recent set of metagenomic assemblies. CONCLUSION: EvalG and EvalCon facilitate the rapid quality control and exploration of PATRIC-annotated draft genomes.

From reference genomes to population genomics: comparing three reference-aligned reduced-representation sequencing pipelines in two wildlife species.

BACKGROUND: Recent advances in genomics have greatly increased research opportunities for non-model species. For wildlife, a growing availability of reference genomes means that population genetics is no longer restricted to a small set of anonymous loci. When used in conjunction with a reference genome, reduced-representation sequencing (RRS) provides a cost-effective method for obtaining reliable diversity information for population genetics. Many software tools have been developed to process RRS data, though few studies of non-model species incorporate genome alignment in calling loci. A commonly-used RRS analysis pipeline, Stacks, has this capacity and so it is timely to compare its utility with existing software originally designed for alignment and analysis of whole genome sequencing data. Here we examine population genetic inferences from two species for which reference-aligned reduced-representation data have been collected. Our two study species are a threatened Australian marsupial (Tasmanian devil Sarcophilus harrisii; declining population) and an Arctic-circle migrant bird (pink-footed goose Anser brachyrhynchus; expanding population). Analyses of these data are compared using Stacks versus two widely-used genomics packages, SAMtools and GATK. We also introduce a custom R script to improve the reliability of single nucleotide polymorphism (SNP) calls in all pipelines and conduct population genetic inferences for non-model species with reference genomes. RESULTS: Although we identified orders of magnitude fewer SNPs in our devil dataset than for goose, we found remarkable symmetry between the two species in our assessment of software performance. For both datasets, all three methods were able to delineate population structure, even with varying numbers of loci. For both species, population structure inferences were influenced by the percent of missing data. CONCLUSIONS: For studies of non-model species with a reference genome, we recommend combining Stacks output with further filtering (as included in our R pipeline) for population genetic studies, paying particular attention to potential impact of missing data thresholds. We recognise SAMtools as a viable alternative for researchers more familiar with this software. We caution against the use of GATK in studies with limited computational resources or time.

An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography.

We present the Metagenomic Intra-species Diversity Analysis System (MIDAS), which is an integrated computational pipeline for quantifying bacterial species abundance and strain-level genomic variation, including gene content and single-nucleotide polymorphisms (SNPs), from shotgun metagenomes. Our method leverages a database of more than 30,000 bacterial reference genomes that we clustered into species groups. These cover the majority of abundant species in the human microbiome but only a small proportion of microbes in other environments, including soil and seawater. We applied MIDAS to stool metagenomes from 98 Swedish mothers and their infants over one year and used rare SNPs to track strains between hosts. Using this approach, we found that although species compositions of mothers and infants converged over time, strain-level similarity diverged. Specifically, early colonizing bacteria were often transmitted from an infant's mother, while late colonizing bacteria were often transmitted from other sources in the environment and were enriched for spore-formation genes. We also applied MIDAS to 198 globally distributed marine metagenomes and used gene content to show that many prevalent bacterial species have population structure that correlates with geographic location. Strain-level genetic variants present in metagenomes clearly reveal extensive structure and dynamics that are obscured when data are analyzed at a coarser taxonomic resolution.

Understanding and overcoming the pitfalls and biases of next-generation sequencing (NGS) methods for use in the routine clinical microbiological diagnostic laboratory.

Recent advancements in next-generation sequencing (NGS) have provided the foundation for modern studies into the composition of microbial communities. The use of these NGS methods allows for the detection and identification of ('difficult-to-culture') microorganisms using a culture-independent strategy. In the field of routine clinical diagnostics however, the application of NGS is currently limited to microbial strain typing for epidemiological purposes only, even though the implementation of NGS for microbial community analysis may yield clinically important information. This lack of NGS implementation is due to many different factors, including issues relating to NGS method standardization and result reproducibility. In this review article, the authors provide a general introduction to the most widely used NGS methods currently available (i.e., targeted amplicon sequencing and shotgun metagenomics) and the strengths and weaknesses of each method is discussed. The focus of the publication then shifts toward 16S rRNA gene NGS methods, which are currently the most cost-effective and widely used NGS methods for research purposes, and are therefore more likely to be successfully implemented into routine clinical diagnostics in the short term. In this respect, the experimental pitfalls and biases created at each step of the 16S rRNA gene NGS workflow are explained, as well as their potential solutions. Finally, a novel diagnostic microbiota profiling platform ('MYcrobiota') is introduced, which was developed by the authors by taking into consideration the pitfalls, biases, and solutions explained in this article. The development of the MYcrobiota, and future NGS methodologies, will help pave the way toward the successful implementation of NGS methodologies into routine clinical diagnostics.