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Advanced Science (Weinheim,... Dec 2021Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture-based diagnosis in clinics is...
Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture-based diagnosis in clinics is increasingly supplemented by metagenomic next-generation-sequencing (mNGS). Here, RNA/cDNA-targeted sequencing (meta-transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta-transcriptomics using third-generation sequencing, mtTGS) to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple-strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct-RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA-targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT.
Topics: Aged; Bronchoalveolar Lavage; Bronchoalveolar Lavage Fluid; Female; High-Throughput Nucleotide Sequencing; Humans; Infections; Male; Metagenome; Metagenomics; Middle Aged; RNA; Sequence Analysis, RNA
PubMed: 34687159
DOI: 10.1002/advs.202102593 -
Genome Research Jul 2015Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions...
Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of "marker" genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.
Topics: Genome, Microbial; Metagenome; Metagenomics
PubMed: 25977477
DOI: 10.1101/gr.186072.114 -
Molecular Biology and Evolution Dec 2021Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales....
Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.
Topics: Databases, Genetic; Genomics; Metagenome; Metagenomics; Molecular Sequence Annotation; Phylogeny; Software
PubMed: 34597405
DOI: 10.1093/molbev/msab293 -
Nature Communications Feb 2021Gut microbiota plays an important role in pig health and production. Still, availability of sequenced genomes and functional information for most pig gut microbes...
Gut microbiota plays an important role in pig health and production. Still, availability of sequenced genomes and functional information for most pig gut microbes remains limited. Here we perform a landscape survey of the swine gut microbiome, spanning extensive sample sources by deep metagenomic sequencing resulting in an expanded gene catalog named pig integrated gene catalog (PIGC), containing 17,237,052 complete genes clustered at 90% protein identity from 787 gut metagenomes, of which 28% are unknown proteins. Using binning analysis, 6339 metagenome-assembled genomes (MAGs) were obtained, which were clustered to 2673 species-level genome bins (SGBs), among which 86% (2309) SGBs are unknown based on current databases. Using the present gene catalog and MAGs, we identified several strain-level differences between the gut microbiome of wild boars and commercial Duroc pigs. PIGC and MAGs provide expanded resources for swine gut microbiome-related research.
Topics: Animals; Bacteria; Female; Gastrointestinal Microbiome; Genes, Microbial; High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Phylogeny; Species Specificity; Swine
PubMed: 33597514
DOI: 10.1038/s41467-021-21295-0 -
Microbial Genomics Nov 2021Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial...
Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.
Topics: Databases, Nucleic Acid; Genome, Bacterial; Metagenome; Metagenomics; Software
PubMed: 34739369
DOI: 10.1099/mgen.0.000685 -
DNA Research : An International Journal... Dec 2023Various microorganisms exist in environments, and each of them has its optimal growth temperature (OGT). The relationship between genomic information and OGT of each...
Various microorganisms exist in environments, and each of them has its optimal growth temperature (OGT). The relationship between genomic information and OGT of each species has long been studied, and one such study revealed that OGT of prokaryotes can be accurately predicted based on the fraction of seven amino acids (IVYWREL) among all encoded amino-acid sequences in its genome. Extending this discovery, we developed a 'Metagenomic Thermometer' as a means of predicting environmental temperature based on metagenomic sequences. Temperature prediction of diverse environments using publicly available metagenomic data revealed that the Metagenomic Thermometer can predict environmental temperatures with small temperature changes and little influx of microorganisms from other environments. The accuracy of the Metagenomic Thermometer was also confirmed by a demonstration experiment using an artificial hot water canal. The Metagenomic Thermometer was also applied to human gut metagenomic samples, yielding a reasonably accurate value for human body temperature. The result further suggests that deep body temperature determines the dominant lineage of the gut community. Metagenomic Thermometer provides a new insight into temperature-driven community assembly based on amino-acid composition rather than microbial taxa.
Topics: Humans; Thermometers; Metagenome; Metagenomics; Genomics
PubMed: 37940329
DOI: 10.1093/dnares/dsad024 -
Current Opinion in Virology Apr 2022Viruses are diverse biological entities that influence all life. Even with limited genome sizes, viruses can manipulate, drive, steal from, and kill their hosts. The... (Review)
Review
Viruses are diverse biological entities that influence all life. Even with limited genome sizes, viruses can manipulate, drive, steal from, and kill their hosts. The field of virus genomics, using sequencing data to understand viral capabilities, has seen significant innovations in recent years. However, with advancements in metagenomic sequencing and related technologies, the bottleneck to discovering and employing the virosphere has become the analysis of genomes rather than generation. With metagenomics rapidly expanding available data, vital components of virus genomes and features are being overlooked, with the issue compounded by lagging databases and bioinformatics methods. Despite the field moving in a positive direction, there are noteworthy points to keep in mind, from how software-based virus genome predictions are interpreted to what information is overlooked by current standards. In this review, we discuss conventions and ideologies that likely need to be revised while continuing forward in the study of virus genomics.
Topics: Genome, Viral; Metagenome; Metagenomics; Software; Viruses
PubMed: 35051682
DOI: 10.1016/j.coviro.2022.101200 -
Annual Review of Virology Sep 2022Over the past 20 years, our knowledge of virus diversity and abundance in subsurface environments has expanded dramatically through application of quantitative... (Review)
Review
Over the past 20 years, our knowledge of virus diversity and abundance in subsurface environments has expanded dramatically through application of quantitative metagenomic approaches. In most subsurface environments, viral diversity and abundance rival viral diversity and abundance observed in surface environments. Most of these viruses are uncharacterized in terms of their hosts and replication cycles. Analysis of accessory metabolic genes encoded by subsurface viruses indicates that they evolved to replicate within the unique features of their environments. The key question remains: What role do these viruses play in the ecology and evolution of the environments in which they replicate? Undoubtedly, as more virologists examine the role of viruses in subsurface environments, new insights will emerge.
Topics: Ecology; Metagenome; Metagenomics; Viruses
PubMed: 36173700
DOI: 10.1146/annurev-virology-093020-015957 -
The Lancet. Microbe Nov 2022Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new... (Review)
Review
Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch's classical postulates remind us about the importance of causative reasoning when considering the relationship between microbes and a disease manifestation. Although causal discovery in observational microbiome data faces many challenges, methodological advances in causal structure learning have improved the potential of data-driven prediction of causal effects in large-scale biological systems. In this Personal View, we show the capability of existing methods for inferring causal effects from metagenomic data, and we highlight ways in which the introduction of causal structures that are more flexible than existing structures offers new opportunities for causal reasoning. Our observations suggest that microbiome research can further benefit from tools developed in the past 5 years in causal discovery and learn from their applications elsewhere.
Topics: Humans; Microbiota; Metagenomics; Causality; Metagenome
PubMed: 36152674
DOI: 10.1016/S2666-5247(22)00186-0 -
Cell Aug 2016Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to... (Review)
Review
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.
Topics: Classification; Computational Biology; Humans; Metagenome; Metagenomics; Microbiota; Models, Statistical
PubMed: 27565341
DOI: 10.1016/j.cell.2016.08.007