Ultra-deep, long-read nanopore sequencing of mock microbial community standards

Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. However, extensive benchmarking studies on nanopore data are lacking. We systematically evaluated performance of bacterial taxonomic classification for metagenomics nanopore sequencing data for several commonly used classifiers, using standardized reference sequence databases, on the largest collection of publicly available data for defined mock communities thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, and high precision/medium recall. Most fall into the first group, although precision can be improved without excessively penalizing recall with suitable abundance filtering. No definitive 'best' classifier emerges, and classifier selection depends on application scope and practical requirements. Although few classifiers designed for long reads exist, they generally exhibit better performance. Our comprehensive benchmarking provides concrete recommendations, supported by publicly available code for reassessment and fine-tuning by other scientists.

Fast and space-efficient taxonomic classification of long reads with hierarchical interleaved XOR filters

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Because of the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memory-efficient querying of long reads. Here, we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches, such as syncmers, for pseudoalignment to classify reads and an expectation-maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms state-of-the-art tools regarding precision while having a similar recall for long-read taxonomic classification. Most notably, Taxor reduces the memory requirements and index size by >50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field.

Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review

This review article offers a comprehensive overview of the current understanding of using metagenomic tools in food microbiome research. It covers the scientific foundation and practical application of genetic analysis techniques for microbial material from food, including bioinformatic analysis and data interpretation. The method discussed in the article for analyzing microorganisms in food without traditional culture methods is known as food metagenomics. This approach, along with other omics technologies such as nutrigenomics, proteomics, metabolomics, and transcriptomics, collectively forms the field of foodomics. Food metagenomics allows swift and thorough examination of bacteria and potential metabolic pathways by utilizing foodomic databases. Despite its established scientific basis and available bioinformatics resources, the research approach of food metagenomics outlined in the article is not yet widely implemented in industry. The authors believe that the integration of next-generation sequencing (NGS) with rapidly advancing digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data will facilitate the widespread adoption of this research strategy in microbial analysis for the food industry. This adoption is expected to enhance food safety and product quality in the near future.

Multiple hybridization events and repeated evolution of homoeologue expression bias in parthenogenetic, polyploid New Zealand stick insects

During hybrid speciation, homoeologues combine in a single genome. Homoeologue expression bias (HEB) occurs when one homoeologue has higher gene expression than another. HEB has been well characterized in plants but rarely investigated in animals, especially invertebrates. Consequently, we have little idea as to the role that HEB plays in allopolyploid invertebrate genomes. If HEB is constrained by features of the parental genomes, then we predict repeated evolution of similar HEB patterns among hybrid genomes formed from the same parental lineages. To address this, we reconstructed the history of hybridization between the New Zealand stick insect genera Acanthoxyla and Clitarchus using a high-quality genome assembly from Clitarchus hookeri to call variants and phase alleles. These analyses revealed the formation of three independent diploid and triploid hybrid lineages between these genera. RNA sequencing revealed a similar magnitude and direction of HEB among these hybrid lineages, and we observed that many enriched functions and pathways were also shared among lineages, consistent with repeated evolution due to parental genome constraints. In most hybrid lineages, a slight majority of the genes involved in mitochondrial function showed HEB towards the maternal homoeologues, consistent with only weak effects of mitonuclear incompatibility. We also observed a proteasome functional enrichment in most lineages and hypothesize this may result from the need to maintain proteostasis in hybrid genomes. Reference bias was a pervasive problem, and we caution against relying on HEB estimates from a single parental reference genome.

Lightweight taxonomic profiling of long-read sequenced metagenomes with Lemur and Magnet

Taxonomic profiling is a ubiquitous task in the analysis of clinical and environmental microbiomes. The advent of long-read sequencing of microbiomes necessitates the development of new taxonomic profilers tailored to long-read shotgun metagenomic datasets. Here, we introduce Lemur and Magnet, a pair of tools optimized for lightweight and accurate taxonomic profiling from long-read shotgun metagenomic datasets. Lemur is a marker-gene based method that leverages an EM algorithm to reduce false positive calls while preserving true positives; Magnet makes detailed presence/absence calls for bacterial genomes based on whole-genome read mapping. The tools work in sequence: Lemur estimates abundances conservatively, and Magnet operates on the genomes of identified organisms to filter out likely false positive taxa. The result is an increase in precision of as much as 70%, which far exceeds competing methods. By operating only on marker genes, Lemur is a comparatively lightweight software. We demonstrate that it can run in minutes to hours on a laptop with 32 GB of RAM, even for large inputs - a crucial feature given the portability of long-read sequencing machines. Furthermore, the marker gene database used by Lemur is only 4 GB and contains information from over 300,000 RefSeq genomes. The reference is available at https://zenodo.org/records/10802546, and the software is open-source and available at https://github.com/treangenlab/lemur.

Benchmarking short-, long- and hybrid-read assemblers for metagenome sequencing of complex microbial communities

Metagenome community analyses, driven by the continued development in sequencing technology, is rapidly providing insights in many aspects of microbiology and becoming a cornerstone tool. Illumina, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are the leading technologies, each with their own advantages and drawbacks. Illumina provides accurate reads at a low cost, but their length is too short to close bacterial genomes. Long reads overcome this limitation, but these technologies produce reads with lower accuracy (ONT) or with lower throughput (PacBio high-fidelity reads). In a critical first analysis step, reads are assembled to reconstruct genomes or individual genes within the community. However, to date, the performance of existing assemblers has never been challenged with a complex mock metagenome. Here, we evaluate the performance of current assemblers that use short, long or both read types on a complex mock metagenome consisting of 227 bacterial strains with varying degrees of relatedness. We show that many of the current assemblers are not suited to handle such a complex metagenome. In addition, hybrid assemblies do not fulfil their potential. We conclude that ONT reads assembled with CANU and Illumina reads assembled with SPAdes offer the best value for reconstructing genomes and individual genes of complex metagenomes, respectively.

Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity

Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.

Can long-read sequencing tackle the barriers, which the next-generation could not? A review

The large-scale heterogeneity of genetic diseases necessitated the deeper examination of nucleotide sequence alterations enhancing the discovery of new targeted drug attack points. The appearance of new sequencing techniques was essential to get more interpretable genomic data. In contrast to the previous short-reads, longer lengths can provide a better insight into the potential health threatening genetic abnormalities. Long-reads offer more accurate variant identification and genome assembly methods, indicating advances in nucleotide deflect-related studies. In this review, we introduce the historical background of sequencing technologies and show their benefits and limits, as well. Furthermore, we highlight the differences between short- and long-read approaches, including their unique advances and difficulties in methodologies and evaluation. Additionally, we provide a detailed description of the corresponding bioinformatics and the current applications.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Considerations for the inclusion of metabarcoding data in the plant protection product risk assessment of the soil microbiome

There is increasing interest in further developing the plant protection product (PPP) environmental risk assessment, particularly within the European Union, to include the assessment of soil microbial community composition, as measured by metabarcoding approaches. However, to date, there has been little discussion as to how this could be implemented in a standardized, reliable, and robust manner suitable for regulatory decision-making. Introduction of metabarcoding-based assessments of the soil microbiome into the PPP risk assessment would represent a significant increase in the degree of complexity of the data that needs to be processed and analyzed in comparison to the existing risk assessment on in-soil organisms. The bioinformatics procedures to process DNA sequences into community compositional data sets currently lack standardization, while little information exists on how these data should be used to generate regulatory endpoints and the ways in which these endpoints should be interpreted. Through a thorough and critical review, we explore these challenges. We conclude that currently, we do not have a sufficient degree of standardization or understanding of the required bioinformatics and data analysis procedures to consider their use in an environmental risk assessment context. However, we highlight critical knowledge gaps and the further research required to understand whether metabarcoding-based assessments of the soil microbiome can be utilized in a statistically and ecologically relevant manner within a PPP risk assessment. Only once these challenges are addressed can we consider if and how we should use metabarcoding as a tool for regulatory decision-making to assess and monitor ecotoxicological effects on soil microorganisms within an environmental risk assessment of PPPs. Integr Environ Assess Manag 2024;20:337-358. © 2023 SETAC.

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads

Motivation

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm.

Results

Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads.

Availability and implementation

HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

The long and short of it: benchmarking viromics using Illumina, Nanopore and PacBio sequencing technologies

Viral metagenomics has fuelled a rapid change in our understanding of global viral diversity and ecology. Long-read sequencing and hybrid assembly approaches that combine long- and short-read technologies are now being widely implemented in bacterial genomics and metagenomics. However, the use of long-read sequencing to investigate viral communities is still in its infancy. While Nanopore and PacBio technologies have been applied to viral metagenomics, it is not known to what extent different technologies will impact the reconstruction of the viral community. Thus, we constructed a mock bacteriophage community of previously sequenced phage genomes and sequenced them using Illumina, Nanopore and PacBio sequencing technologies and tested a number of different assembly approaches. When using a single sequencing technology, Illumina assemblies were the best at recovering phage genomes. Nanopore- and PacBio-only assemblies performed poorly in comparison to Illumina in both genome recovery and error rates, which both varied with the assembler used. The best Nanopore assembly had errors that manifested as SNPs and INDELs at frequencies 41 and 157 % higher than found in Illumina only assemblies, respectively. While the best PacBio assemblies had SNPs at frequencies 12 and 78 % higher than found in Illumina-only assemblies, respectively. Despite high-read coverage, long-read-only assemblies recovered a maximum of one complete genome from any assembly, unless reads were down-sampled prior to assembly. Overall the best approach was assembly by a combination of Illumina and Nanopore reads, which reduced error rates to levels comparable with short-read-only assemblies. When using a single technology, Illumina only was the best approach. The differences in genome recovery and error rates between technology and assembler had downstream impacts on gene prediction, viral prediction, and subsequent estimates of diversity within a sample. These findings will provide a starting point for others in the choice of reads and assembly algorithms for the analysis of viromes.

Comparative analysis of metagenomic classifiers for long-read sequencing datasets

BACKGROUND

Long reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001 to 20% and six real gut microbiomes.

RESULTS

General-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the host's genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports.

CONCLUSION

The findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.

Determining Potential Link between Environmental and Clinical Isolates of Cryptococcus neoformans/Cryptococcus gattii Species Complexes Using Phenotypic and Genotypic Characterisation

Opportunistic infections due to Cryptococcus neoformans and C. gattii species complexes continue to rise unabated among HIV/AIDS patients, despite improved antifungal therapies. Here, we collected a total of 20 environmental and 25 presumptive clinical cryptococcal isolates from cerebrospinal fluid (CSF) samples of 175 patients enrolled in an ongoing clinical trial Ambition 1 Project (Botswana-Harvard Partnership). Identity confirmation of the isolates was done using MALDI-TOF MS and PCR. We describe the diversity of the isolates by PCR fingerprinting and sequencing (Oxford Nanopore Technology) of the intergenic spacer region. Mating types of the isolates were determined by amplification of the MAT locus. We report an unusual prevalence of 42.1% of C. neoformans x C. deneoformans hybrids Serotype AD (n = 16), followed by 39.5% of C. neoformans Serotype A (n = 15), 5.3% of C. deneoformans, Serotype D (n = 2), 7.9% of C. gattii (n = 3), and 5.3% of C. tetragattii (n = 2) in 38 representative isolates that have been characterized. Mating type-specific PCR performed on 38 representative environmental and clinical isolates revealed that 16 (42.1%) were MATa/MATα hybrids, 17 (44.7%) were MATα, and five (13.2%) possessed MATa mating type. We used conventional and NGS platforms to demonstrate a potential link between environmental and clinical isolates and lay a foundation to further describe mating patterns/history in Botswana.

Correcting modification-mediated errors in nanopore sequencing by nucleotide demodification and reference-based correction

The accuracy of Oxford Nanopore Technology (ONT) sequencing has significantly improved thanks to new flowcells, sequencing kits, and basecalling algorithms. However, novel modification types untrained in the basecalling models can seriously reduce the quality. Here we reports a set of ONT-sequenced genomes with unexpected low quality due to novel modification types. Demodification by whole-genome amplification significantly improved the quality but lost the epigenome. We also developed a reference-based method, Modpolish, for correcting modification-mediated errors while retaining the epigenome when a sufficient number of closely-related genomes is publicly available (default: top 20 genomes with at least 95% identity). Modpolish not only significantly improved the quality of in-house sequenced genomes but also public datasets sequenced by R9.4 and R10.4 (simplex). Our results suggested that novel modifications are prone to ONT systematic errors. Nevertheless, these errors are correctable by nucleotide demodification or Modpolish without prior knowledge of modifications.

Development of a portable on-site applicable metagenomic data generation workflow for enhanced pathogen and antimicrobial resistance surveillance

Rapid, accurate and comprehensive diagnostics are essential for outbreak prevention and pathogen surveillance. Real-time, on-site metagenomics on miniaturized devices, such as Oxford Nanopore Technologies MinION sequencing, could provide a promising approach. However, current sample preparation protocols often require substantial equipment and dedicated laboratories, limiting their use. In this study, we developed a rapid on-site applicable DNA extraction and library preparation approach for nanopore sequencing, using portable devices. The optimized method consists of a portable mechanical lysis approach followed by magnetic bead-based DNA purification and automated sequencing library preparation, and resulted in a throughput comparable to a current optimal, laboratory-based protocol using enzymatic digestion to lyse cells. By using spike-in reference communities, we compared the on-site method with other workflows, and demonstrated reliable taxonomic profiling, despite method-specific biases. We also demonstrated the added value of long-read sequencing by recovering reads containing full-length antimicrobial resistance genes, and attributing them to a host species based on the additional genomic information they contain. Our method may provide a rapid, widely-applicable approach for microbial detection and surveillance in a variety of on-site settings.

A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens

Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources.

The newest Oxford Nanopore R10.4.1 full-length 16S rRNA sequencing enables the accurate resolution of species-level microbial community profiling

The long-read amplicon provides a species-level solution for the community. With the improvement of nanopore flowcells, the accuracy of Oxford Nanopore Technologies (ONT) R10.4.1 has been substantially enhanced, with an average of approximately 99%. To evaluate its effectiveness on amplicons, three types of microbiomes were analyzed by 16S ribosomal RNA (hereinafter referred to as "16S") amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1) in the current study. We showed the error rate, recall, precision, and bias index in the mock sample. The error rate of ONT R10.4.1 was greatly reduced, with a better recall in the case of the synthetic community. Meanwhile, in different types of environmental samples, ONT R10.4.1 analysis resulted in a composition similar to Pacbio data. We found that classification tools and databases influence ONT data. Based on these results, we conclude that the ONT R10.4.1 16S amplicon can also be used for application in environmental samples. IMPORTANCE The long-read amplicon supplies the community with a species-level solution. Due to the high error rate of nanopore sequencing early on, it has not been frequently used in 16S studies. Oxford Nanopore Technologies (ONT) introduced the R10.4.1 flowcell with Q20+ reagent to achieve more than 99% accuracy as sequencing technology advanced. However, there has been no published study on the performance of commercial PromethION sequencers with R10.4.1 flowcells on 16S sequencing or on the impact of accuracy improvement on taxonomy (R9.4.1 to R10.4.1) using 16S ONT data. In this study, three types of microbiomes were investigated by 16S ribosomal RNA (rRNA) amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1). In the mock sample, we displayed the error rate, recall, precision, and bias index. We observed that the error rate in ONT R10.4.1 is significantly lower, especially when deletions are involved. First and foremost, R10.4.1 and Pacific Bioscience platforms reveal a similar microbiome in environmental samples. This study shows that the R10.4.1 full-length 16S rRNA sequences allow for species identification of environmental microbiota.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

cgMSI: pathogen detection within species from nanopore metagenomic sequencing data

BACKGROUND

Metagenomic sequencing is an unbiased approach that can potentially detect all the known and unidentified strains in pathogen detection. Recently, nanopore sequencing has been emerging as a highly potential tool for rapid pathogen detection due to its fast turnaround time. However, identifying pathogen within species is nontrivial for nanopore sequencing data due to the high sequencing error rate.

RESULTS

We developed the core gene alleles metagenome strain identification (cgMSI) tool, which uses a two-stage maximum a posteriori probability estimation method to detect pathogens at strain level from nanopore metagenomic sequencing data at low computational cost. The cgMSI tool can accurately identify strains and estimate relative abundance at 1× coverage.

CONCLUSIONS

We developed cgMSI for nanopore metagenomic pathogen detection within species. cgMSI is available at https://github.com/ZHU-XU-xmu/cgMSI .

Comparison of 6 DNA extraction methods for isolation of high yield of high molecular weight DNA suitable for shotgun metagenomics Nanopore sequencing to detect bacteria

BACKGROUND

Oxford Nanopore Technologies (ONT) offers an accessible platform for long-read sequencing, which improves the reconstruction of genomes and helps to resolve complex genomic contexts, especially in the case of metagenome analysis. To take the best advantage of long-read sequencing, DNA extraction methods must be able to isolate pure high molecular weight (HMW) DNA from complex metagenomics samples, without introducing any bias. New methods released on the market, and protocols developed at the research level, were specifically designed for this application and need to be assessed.

RESULTS

In this study, with different bacterial cocktail mixes, analyzed as pure or spiked in a synthetic fecal matrix, we evaluated the performances of 6 DNA extraction methods using various cells lysis and purification techniques, from quick and easy, to more time-consuming and gentle protocols, including a portable method for on-site application. In addition to the comparison of the quality, quantity and purity of the extracted DNA, the performance obtained when doing Nanopore sequencing on a MinION flow cell was also tested. From the obtained results, the Quick-DNA HMW MagBead Kit (Zymo Research) was selected as producing the best yield of pure HMW DNA. Furthermore, this kit allowed an accurate detection, by Nanopore sequencing, of almost all the bacterial species present in a complex mock community.

CONCLUSION

Amongst the 6 tested methods, the Quick-DNA HMW MagBead Kit (Zymo Research) was considered as the most suitable for Nanopore sequencing and would be recommended for bacterial metagenomics studies using this technology.

Chromosomal organization of biosynthetic gene clusters, including those of nine novel species, suggests plasticity of myxobacterial specialized metabolism

Introduction

Natural products discovered from bacteria provide critically needed therapeutic leads for drug discovery, and myxobacteria are an established source for metabolites with unique chemical scaffolds and biological activities. Myxobacterial genomes accommodate an exceptional number and variety of biosynthetic gene clusters (BGCs) which encode for features involved in specialized metabolism.

Methods

In this study, we describe the collection, sequencing, and genome mining of 20 myxobacteria isolated from rhizospheric soil samples collected in North America.

Results

Nine isolates were determined to be novel species of myxobacteria including representatives from the genera Archangium, Myxococcus, Nannocystis, Polyangium, Pyxidicoccus, Sorangium, and Stigmatella. Growth profiles, biochemical assays, and descriptions were provided for all proposed novel species. We assess the BGC content of all isolates and observe differences between Myxococcia and Polyangiia clusters.

Discussion

Continued discovery and sequencing of novel myxobacteria from the environment provide BGCs for the genome mining pipeline. Utilizing complete or near-complete genome sequences, we compare the chromosomal organization of BGCs of related myxobacteria from various genera and suggest that the spatial proximity of hybrid, modular clusters contributes to the metabolic adaptability of myxobacteria.

Rapid metagenomic sequencing for diagnosis and antimicrobial sensitivity prediction of canine bacterial infections

Antimicrobial resistance is a major threat to human and animal health. There is an urgent need to ensure that antimicrobials are used appropriately to limit the emergence and impact of resistance. In the human and veterinary healthcare setting, traditional culture and antimicrobial sensitivity testing typically requires 48-72 h to identify appropriate antibiotics for treatment. In the meantime, broad-spectrum antimicrobials are often used, which may be ineffective or impact non-target commensal bacteria. Here, we present a rapid, culture-free, diagnostics pipeline, involving metagenomic nanopore sequencing directly from clinical urine and skin samples of dogs. We have planned this pipeline to be versatile and easily implementable in a clinical setting, with the potential for future adaptation to different sample types and animals. Using our approach, we can identify the bacterial pathogen present within 5 h, in some cases detecting species which are difficult to culture. For urine samples, we can predict antibiotic sensitivity with up to 95 % accuracy. Skin swabs usually have lower bacterial abundance and higher host DNA, confounding antibiotic sensitivity prediction; an additional host depletion step will likely be required during the processing of these, and other types of samples with high levels of host cell contamination. In summary, our pipeline represents an important step towards the design of individually tailored veterinary treatment plans on the same day as presentation, facilitating the effective use of antibiotics and promoting better antimicrobial stewardship.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.

Nanopore-Based Metagenomic Sequencing in Respiratory Tract Infection: A Developing Diagnostic Platform

Respiratory tract infection (RTI) remains a significant cause of morbidity and mortality across the globe. The optimal management of RTI relies upon timely pathogen identification via evaluation of respiratory samples, a process which utilises traditional culture-based methods to identify offending microorganisms. This process can be slow and often prolongs the use of broad-spectrum antimicrobial therapy, whilst also delaying the introduction of targeted therapy as a result. Nanopore sequencing (NPS) of respiratory samples has recently emerged as a potential diagnostic tool in RTI. NPS can identify pathogens and antimicrobial resistance profiles with greater speed and efficiency than traditional sputum culture-based methods. Increased speed to pathogen identification can improve antimicrobial stewardship by reducing the use of broad-spectrum antibiotic therapy, as well as improving overall clinical outcomes. This new technology is becoming more affordable and accessible, with some NPS platforms requiring minimal sample preparation and laboratory infrastructure. However, questions regarding clinical utility and how best to implement NPS technology within RTI diagnostic pathways remain unanswered. In this review, we introduce NPS as a technology and as a diagnostic tool in RTI in various settings, before discussing the advantages and limitations of NPS, and finally what the future might hold for NPS platforms in RTI diagnostics.

High-resolution single-molecule long-fragment rRNA gene amplicon sequencing of bacterial and eukaryotic microbial communities

Sequencing of hypervariable regions as well as internal transcribed spacer regions of ribosomal RNA genes (rDNA) is broadly used to identify bacteria and fungi, but taxonomic and phylogenetic resolution is hampered by insufficient sequencing length using high throughput, cost-efficient second-generation sequencing. We developed a method to obtain nearly full-length rDNA by assembling single DNA molecules combining DNA co-barcoding with single-tube long fragment read technology and second-generation sequencing. Benchmarking was performed using mock bacterial and fungal communities as well as two forest soil samples. All mock species rDNA were successfully recovered with identities above 99.5% compared to the reference sequences. From the soil samples we obtained good coverage with identification of more than 20,000 unknown species, as well as high abundance correlation between replicates. This approach provides a cost-effective method for obtaining extensive and accurate information on complex environmental microbial communities.

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim

BACKGROUND

Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, sequencing platform-specific challenges, including high base-call error rate, nonuniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical algorithms. The use of simulated datasets with characteristics that are true to the sequencing platform under evaluation is a cost-effective way to assess the performance of bioinformatics tools with the ground truth in a controlled environment.

RESULTS

Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. It improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. Meta-NanoSim can simulate complex microbial communities composed of both linear and circular genomes and can stream reference genomes from online servers directly. Simulated datasets showed high congruence with experimental data in terms of read length, error profiles, and abundance levels. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenome assembly benchmarking task.

CONCLUSIONS

The Meta-NanoSim characterization module investigates read features, including chimeric information and abundance levels, while the simulation module simulates large and complex multisample microbial communities with different abundance profiles. All trained models and the software are freely accessible at GitHub: https://github.com/bcgsc/NanoSim.

Chromosomal organization of biosynthetic gene clusters suggests plasticity of myxobacterial specialized metabolism including descriptions for nine novel species: Archangium lansinium sp. nov., Myxococcus landrumus sp. nov., Nannocystis bainbridgea sp. nov., Nannocystis poenicansa sp. nov., Nannocystis radixulma sp. nov., Polyangium mundeleinium sp. nov., Pyxidicoccus parkwaysis sp. nov., Sorangium aterium sp. nov., Stigmatella ashevillena sp. nov

Natural products discovered from bacteria provide critically needed therapeutic leads for drug discovery, and myxobacteria are an established source for metabolites with unique chemical scaffolds and biological activities. Myxobacterial genomes accommodate an exceptional number and variety of biosynthetic gene clusters (BGCs) which encode for features involved in specialized metabolism. Continued discovery and sequencing of novel myxobacteria from the environment provides BGCs for the genome mining pipeline. Herein, we describe the collection, sequencing, and genome mining of 20 myxobacteria isolated from rhizospheric soil samples collected in North America. Nine isolates where determined to be novel species of myxobacteria including representatives from the genera Archangium, Myxococcus, Nannocystis, Polyangium, Pyxidicoccus, Sorangium, and Stigmatella. Growth profiles, biochemical assays, and descriptions are provided for all proposed novel species. We assess the BGC content of all isolates and observe differences between Myxococcia and Polyangiia clusters. Utilizing complete or near complete genome sequences we compare the chromosomal organization of BGCs of related myxobacteria from various genera and suggest spatial proximity of hybrid, modular clusters contributes to the metabolic adaptability of myxobacteria.

Identifying Taxonomic Units in Metagenomic DNA Streams on Mobile Devices

With the emergence of portable DNA sequencers, such as Oxford Nanopore Technology MinION, metagenomic DNA sequencing can be performed in real-time and directly in the field. However, because metagenomic DNA analysis tasks, e.g., classification, taxonomic units assignment, etc., are compute and memory intensive, and the available methods are designed for batch processing, the current metagenomic tools are not well suited for mobile devices. In this work, we propose a new memory-efficient approach to identify Operational Taxonomic Units (OTUs) in metagenomic DNA streams on mobile devices. Our method is based on finding connected components in overlap graphs constructed over a real-time stream of long DNA reads as produced by the MinION platform. We propose an efficient algorithm to maintain connected components when an overlap graph is streamed and show how redundant information can be removed from the stream by transitive closures. We also propose how our algorithms can be integrated into a larger DNA analysis pipeline tailored for mobile computing. Through experiments on simulated and real-world metagenomic data, executed on the actual mobile device, we demonstrate that our resulting solution is able to recover OTUs with high precision. Our experiments also demonstrate the compounding benefits of introducing feedback loops in the DNA analysis pipeline.

Long amplicon HiFi sequencing for mitochondrial DNA genomes

Long-read sequencing technology is a powerful approach with application in various genetic and genomic research. Herein, we developed the pipeline for long amplicon high-fidelity (HiFi) sequencing and then applied it for sequencing mitochondrial DNA (mtDNA) genomes from pools of 79 Tibetan Mastiffs. We amplified the mtDNA genome with long-range PCR using two pairs of primers. Two rounds of circular consensus sequencing (CCS) were conducted and their accuracy was evaluated. The results indicate that the second round of CCS can improve the accuracy of HiFi reads. In addition, the analysis of 79 high-quality mtDNA genomes shows the Tibetan Mastiffs from outside of the Tibetan Plateau experienced hybridization with other dogs. The high quality reads generator (HQGR) software is provided to facilitate data analyses, which is publicly accessible on GitHub (https://github.com/Caizf-script/HQGR). Our long amplicon HiFi sequencing pipeline can also be applied in various target enrichment strategies for small genomes and candidate genes.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.

Genome-Wide Association with Uncertainty in the Genetic Similarity Matrix

Genome-wide association studies (GWASs) are often confounded by population stratification and structure. Linear mixed models (LMMs) are a powerful class of methods for uncovering genetic effects, while controlling for such confounding. LMMs include random effects for a genetic similarity matrix, and they assume that a true genetic similarity matrix is known. However, uncertainty about the phylogenetic structure of a study population may degrade the quality of LMM results. This may happen in bacterial studies in which the number of samples or loci is small, or in studies with low-quality genotyping. In this study, we develop methods for linear mixed models in which the genetic similarity matrix is unknown and is derived from Markov chain Monte Carlo estimates of the phylogeny. We apply our model to a GWAS of multidrug resistance in tuberculosis, and illustrate our methods on simulated data.

Strategies and tools in illumina and nanopore-integrated metagenomic analysis of microbiome data

Metagenomic strategy serves as the foundation for the ecological exploration of novel bioresources (e.g., industrial enzymes and bioactive molecules) and biohazards (e.g., pathogens and antibiotic resistance genes) in natural and engineered microbial systems across multiple disciplines. Recent advancements in sequencing technology have fostered rapid development in the field of microbiome research where an increasing number of studies have applied both illumina short reads (SRs) and nanopore long reads (LRs) sequencing in their metagenomic workflow. However, given the high complexity of an environmental microbiome data set and the bioinformatic challenges caused by the unique features of these sequencing technologies, integrating SRs and LRs is not as straightforward as one might assume. The fast renewal of existing tools and growing diversity of new algorithms make access to this field even more difficult. Therefore, here we systematically summarized the complete workflow from DNA extraction to data processing strategies for applying illumina and nanopore-integrated metagenomics in the investigation in environmental microbiomes. Overall, this review aims to provide a timely knowledge framework for researchers that are interested in or are struggling with the SRs and LRs integration in their metagenomic analysis. The discussions presented will facilitate improved ecological understanding of community functionalities and assembly of natural, engineered, and human microbiomes, benefiting researchers from multiple disciplines.

Recovery and Analysis of Long-Read Metagenome-Assembled Genomes

The development of long-read nucleic acid sequencing is beginning to make very substantive impact on the conduct of metagenome analysis, particularly in relation to the problem of recovering the genomes of member species of complex microbial communities. Here we outline bioinformatics workflows for the recovery and characterization of complete genomes from long-read metagenome data and some complementary procedures for comparison of cognate draft genomes and gene quality obtained from short-read sequencing and long-read sequencing.

LMAS: evaluating metagenomic short de novo assembly methods through defined communities

BACKGROUND

The de novo assembly of raw sequence data is key in metagenomic analysis. It allows recovering draft genomes from a pool of mixed raw reads, yielding longer sequences that offer contextual information and provide a more complete picture of the microbial community.

FINDINGS

To better compare de novo assemblers for metagenomic analysis, LMAS (Last Metagenomic Assembler Standing) was developed as a flexible platform allowing users to evaluate assembler performance given known standard communities. Overall, in our test datasets, k-mer De Bruijn graph assemblers outperformed the alternative approaches but came with a greater computational cost. Furthermore, assemblers branded as metagenomic specific did not consistently outperform other genomic assemblers in metagenomic samples. Some assemblers still in use, such as ABySS, MetaHipmer2, minia, and VelvetOptimiser, perform relatively poorly and should be used with caution when assembling complex samples. Meaningful strain resolution at the single-nucleotide polymorphism level was not achieved, even by the best assemblers tested.

CONCLUSIONS

The choice of a de novo assembler depends on the computational resources available, the replicon of interest, and the major goals of the analysis. No single assembler appeared an ideal choice for short-read metagenomic prokaryote replicon assembly, each showing specific strengths. The choice of metagenomic assembler should be guided by user requirements and characteristics of the sample of interest, and LMAS provides an interactive evaluation platform for this purpose. LMAS is open source, and the workflow and its documentation are available at https://github.com/B-UMMI/LMAS and https://lmas.readthedocs.io/, respectively.

Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets

BACKGROUND

Long-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.

RESULTS

Our results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.

CONCLUSIONS

Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

MetaTrass: A high-quality metagenome assembler of the human gut microbiome by cobarcoding sequencing reads

Metagenomic evidence of great genetic diversity within the nonconserved regions of the human gut microbial genomes appeals for new methods to elucidate the species-level variability at high resolution. However, current approaches cannot satisfy this methodologically challenge. In this study, we proposed an efficient binning-first-and-assembly-later strategy, named MetaTrass, to recover high-quality species-resolved genomes based on public reference genomes and the single-tube long fragment read (stLFR) technology, which enables cobarcoding. MetaTrass can generate genomes with longer contiguity, higher completeness, and lower contamination than those produced by conventional assembly-first-and-binning-later strategies. From a simulation study on a mock microbial community, MetaTrass showed the potential to improve the contiguity of assembly from kb to Mb without accuracy loss, as compared to other methods based on the next-generation sequencing technology. From four human fecal samples, MetaTrass successfully retrieved 178 high-quality genomes, whereas only 58 ones were provided by the optimal performance of other conventional strategies. Most importantly, these high-quality genomes confirmed the high level of genetic diversity among different samples and unveiled much more. MetaTrass was designed to work with metagenomic reads sequenced by stLFR technology, but is also applicable to other types of cobarcoding libraries. With the high capability of assembling high-quality genomes of metagenomic data sets, MetaTrass seeks to facilitate the study of spatial characters and dynamics of complex microbial communities at enhanced resolution. The open-source code of MetaTrass is available at https://github.com/BGI-Qingdao/MetaTrass.

Screening of colistin-resistant bacteria in livestock animals from France

Colistin is frequently used as a growth factor or treatment against infectious bacterial diseases in animals. The Veterinary Division of the European Medicines Agency (EMA) restricted colistin use as a second-line treatment to reduce colistin resistance. In 2020, 282 faecal samples were collected from chickens, cattle, sheep, goats, and pigs in the south of France. In order to track the emergence of mobilized colistin resistant (mcr) genes in pigs, 111 samples were re-collected in 2021 and included pig faeces, food, and water from the same location. All samples were cultured in a selective Lucie Bardet Jean-Marc Rolain (LBJMR) medium and colonies were identified using MALDI-TOF mass spectrometry and then antibiotic susceptibility tests were performed. PCR and Sanger sequencing were performed to screen for the presence of mcr genes. The selective culture revealed the presence of 397 bacteria corresponding to 35 different bacterial species including Gram-negative and Gram-positive. Pigs had the highest prevalence of colistin-resistant bacteria with an abundance of intrinsically colistin-resistant bacteria and from these samples one strain harbouring both mcr-1 and mcr-3 has been isolated. The second collection allowed us to identify 304 bacteria and revealed the spread of mcr-1 and mcr-3 in pigs. In the other samples, naturally, colistin-resistant bacteria were more frequent, nevertheless the mcr-1 variant was the most abundant gene found in chicken, sheep, and goat samples and one cattle sample was positive for the mcr-3 gene. Animals are potential reservoir of colistin-resistant bacteria which varies from one animal to another. Interventions and alternative options are required to reduce the emergence of colistin resistance and to avoid zoonotic transmissions.

A rapid bacterial pathogen and antimicrobial resistance diagnosis workflow using Oxford nanopore adaptive sequencing method

Metagenomic sequencing analysis (mNGS) has been implemented as an alternative approach for pathogen diagnosis in recent years, which is independent of cultivation and is able to identify all potential antibiotic resistance genes (ARGs). However, current mNGS methods have to deal with low amounts of prokaryotic deoxyribonucleic acid (DNA) and high amounts of host DNA in clinical samples, which significantly decrease the overall microbial detection resolution. The recently released nanopore adaptive sampling (NAS) technology facilitates immediate mapping of individual nucleotides to a given reference as each molecule is sequenced. User-defined thresholds allow for the retention or rejection of specific molecules, informed by the real-time reference mapping results, as they are physically passing through a given sequencing nanopore. We developed a metagenomics workflow for ultra-sensitive diagnosis of bacterial pathogens and ARGs from clinical samples, which is based on the efficient selective 'human host depletion' NAS sequencing, real-time species identification and species-specific resistance gene prediction. Our method increased the microbial sequence yield at least 8-fold in all 21 sequenced clinical Bronchoalveolar Lavage Fluid (BALF) samples (4.5 h from sample to result) and accurately detected the ARGs at species level. The species-level positive percent agreement between metagenomic sequencing and laboratory culturing was 100% (16/16) and negative percent agreement was 100% (5/5) in our approach. Further work is required for a more robust validation of our approach with large sample size to allow its application to other infection types.

MinION Whole-Genome Sequencing in Resource-Limited Settings: Challenges and Opportunities

Purpose of Review

The introduction of MinION whole-genome sequencing technology greatly increased and simplified complete genome sequencing in various fields of science across the globe. Sequences have been generated from complex organisms to microorganisms and are stored in genome databases that are readily accessible by researchers. Various new software for genome analysis, along with upgrades to older software packages, are being generated. New protocols are also being validated that enable WGS technology to be rapidly and increasingly used for sequencing in field settings.

Recent Findings

MinION WGS technology has been implemented in developed countries due to its advantages: portability, real-time analysis, and lower cost compared to other sequencing technologies. While these same advantages are critical in developing countries, MinION WGS technology is still under-utilized in resource-limited settings.

Summary

In this review, we look at the applications, advantages, challenges, and opportunities of using MinION WGS in resource-limited settings.

Benchmarking second and third-generation sequencing platforms for microbial metagenomics

Shotgun metagenomic sequencing is a common approach for studying the taxonomic diversity and metabolic potential of complex microbial communities. Current methods primarily use second generation short read sequencing, yet advances in third generation long read technologies provide opportunities to overcome some of the limitations of short read sequencing. Here, we compared seven platforms, encompassing second generation sequencers (Illumina HiSeq 300, MGI DNBSEQ-G400 and DNBSEQ-T7, ThermoFisher Ion GeneStudio S5 and Ion Proton P1) and third generation sequencers (Oxford Nanopore Technologies MinION R9 and Pacific Biosciences Sequel II). We constructed three uneven synthetic microbial communities composed of up to 87 genomic microbial strains DNAs per mock, spanning 29 bacterial and archaeal phyla, and representing the most complex and diverse synthetic communities used for sequencing technology comparisons. Our results demonstrate that third generation sequencing have advantages over second generation platforms in analyzing complex microbial communities, but require careful sequencing library preparation for optimal quantitative metagenomic analysis. Our sequencing data also provides a valuable resource for testing and benchmarking bioinformatics software for metagenomics.

Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics

Cocoa beans fermentation is a spontaneous process, essential for the generation of quality starting material for fine chocolate production. The understanding of this process has been studied by the application of high-throughput sequencing technologies, which grants a better assessment of the different microbial taxa and their genes involved in this microbial succession. The present study used shotgun metagenomics to determine the enzyme-coding genes of the microbiota found in two different groups of cocoa beans varieties during the fermentation process. The statistical evaluation of the most abundant genes in each group and time studied allowed us to identify the potential metabolic pathways involved in the success of the different microorganisms. The results showed that, albeit the distinction between the initial (0 h) microbiota of each varietal group was clear, throughout fermentation (24-144 h) this difference disappeared, indicating the existence of selection pressures. Changes in the microbiota enzyme-coding genes over time pointed to the distinct ordering of fermentation at 24-48 h (T1), 72-96 h (T2), and 120-144 h (T3). At T1, the significantly more abundant enzyme-coding genes were related to threonine metabolism and those genes related to the glycolytic pathway, explained by the abundance of sugars in the medium. At T2, the genes linked to the metabolism of ceramides and hopanoids lipids were clearly dominant, which are associated with the resistance of microbial species to extreme temperatures and pH values. In T3, genes linked to trehalose metabolism, related to the response to heat stress, dominated. The results obtained in this study provided insights into the potential functionality of microbial community succession correlated to gene function, which could improve cocoa processing practices to ensure the production of more stable quality end products.

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes

BACKGROUND

Metagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq).

RESULTS

Using technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT.

CONCLUSIONS

TELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

Environmental nucleic acids: A field-based comparison for monitoring freshwater habitats using eDNA and eRNA

Nucleic acids released by organisms and isolated from environmental substrates are increasingly being used for molecular biomonitoring. While environmental DNA (eDNA) has received much attention, the potential of environmental RNA as a biomonitoring tool remains under-explored. Several recent studies using paired DNA and RNA metabarcoding of bulk samples suggest that RNA might better reflect "metabolically active" parts of the community. However, such studies mainly capture organismal eDNA and eRNA. For larger eukaryotes, isolation of extra-organismal RNA will be important, but viability needs to be examined in a field-based setting. In this study we evaluate (a) whether extra-organismal eRNA release from macroeukaryotes can be detected given its supposedly rapid degradation, and (b) if the same field collection methods for eDNA can be applied to eRNA. We collected eDNA and eRNA from water in lakes where fish community composition is well documented, enabling a comparison between the two nucleic acids in two different seasons with monitoring using conventional methods. We found that eRNA is released from macroeukaryotes and can be filtered from water and metabarcoded in a similar manner as eDNA to reliably provide species composition information. eRNA had a small but significantly greater true positive rate than eDNA, indicating that it correctly detects more species known to exist in the lakes. Given relatively small differences between the two molecules in describing fish community composition, we conclude that if eRNA provides significant advantages in terms of lability, it is a strong candidate to add to the suite of molecular monitoring tools.

KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing

Nanopore technology enables portable, real-time sequencing of microbial populations from clinical and ecological samples. An emerging healthcare application for Nanopore includes point-of-care, timely identification of antibiotic resistance genes (ARGs) to help developing targeted treatments of bacterial infections, and monitoring resistant outbreaks in the environment. While several computational tools exist for classifying ARGs from sequencing data, to date (2022) none have been developed for mobile devices. We present here KARGAMobile, a mobile app for portable, real-time, easily interpretable analysis of ARGs from Nanopore sequencing. KARGAMobile is the porting of an existing ARG identification tool named KARGA; it retains the same algorithmic structure, but it is optimized for mobile devices. Specifically, KARGAMobile employs a compressed ARG reference database and different internal data structures to save RAM usage. The KARGAMobile app features a friendly graphical user interface that guides through file browsing, loading, parameter setup, and process execution. More importantly, the output files are post-processed to create visual, printable and shareable reports, aiding users to interpret the ARG findings. The difference in classification performance between KARGAMobile and KARGA is minimal (96.2% vs. 96.9% f-measure on semi-synthetic datasets of 1 million reads with known resistance ground truth). Using real Nanopore experiments, KARGAMobile processes on average 1 GB data every 23-48 min (targeted sequencing - metagenomics), with peak RAM usage below 500MB, independently from input file sizes, and an average temperature of 49°C after 1 h of continuous data processing. KARGAMobile is written in Java and is available at https://github.com/Ruiz-HCI-Lab/KargaMobile under the MIT license.

NanoRTax, a real-time pipeline for taxonomic and diversity analysis of nanopore 16S rRNA amplicon sequencing data

Background

The study of microbial communities and their applications have been leveraged by advances in sequencing techniques and bioinformatics tools. The Oxford Nanopore Technologies long-read sequencing by nanopores provides a portable and cost-efficient platform for sequencing assays. While this opens the possibility of sequencing applications outside specialized environments and real-time analysis of data, complementing the existing efficient library preparation protocols with streamlined bioinformatic workflows is required.

Results

Here we present NanoRTax, a Nextflow pipeline for nanopore 16S rRNA gene amplicon data that features state-of-the-art taxonomic classification tools and real-time capability. The pipeline is paired with a web-based visual interface to enable user-friendly inspections of the experiment in progress. NanoRTax workflow and a simulated real-time analysis were used to validate the prediction of adult Intensive Care Unit patient mortality based on full-length 16S rRNA sequencing data from respiratory microbiome samples.

Conclusions

This constitutes a proof-of-concept simulation study of how real-time bioinformatic workflows could be used to shorten the turnaround times in critical care settings and provides an instrument for future research on early-response strategies for sepsis.

Chromosome-scale genome assembly of Camellia sinensis combined with multi-omics provides insights into its responses to infestation with green leafhoppers

The tea plant (Camellia sinensis) is an important economic crop, which is becoming increasingly popular worldwide, and is now planted in more than 50 countries. Tea green leafhopper is one of the major pests in tea plantations, which can significantly reduce the yield and quality of tea during the growth of plant. In this study, we report a genome assembly for DuyunMaojian tea plants using a combination of Oxford Nanopore Technology PromethION^™ with high-throughput chromosome conformation capture technology and used multi-omics to study how the tea plant responds to infestation with tea green leafhoppers. The final genome was 3.08 Gb. A total of 2.97 Gb of the genome was mapped to 15 pseudo-chromosomes, and 2.79 Gb of them could confirm the order and direction. The contig N50, scaffold N50 and GC content were 723.7 kb, 207.72 Mb and 38.54%, respectively. There were 2.67 Gb (86.77%) repetitive sequences, 34,896 protein-coding genes, 104 miRNAs, 261 rRNA, 669 tRNA, and 6,502 pseudogenes. A comparative genomics analysis showed that DuyunMaojian was the most closely related to Shuchazao and Yunkang 10, followed by DASZ and tea-oil tree. The multi-omics results indicated that phenylpropanoid biosynthesis, α-linolenic acid metabolism, flavonoid biosynthesis and 50 differentially expressed genes, particularly peroxidase, played important roles in response to infestation with tea green leafhoppers (Empoasca vitis Göthe). This study on the tea tree is highly significant for its role in illustrating the evolution of its genome and discovering how the tea plant responds to infestation with tea green leafhoppers will contribute to a theoretical foundation to breed tea plants resistant to insects that will ultimately result in an increase in the yield and quality of tea.

Long-Read-Resolved, Ecosystem-Wide Exploration of Nucleotide and Structural Microdiversity of Lake Bacterioplankton Genomes

Reconstruction of metagenome-assembled genomes (MAGs) has become a fundamental approach in microbial ecology. However, a MAG is hardly complete and overlooks genomic microdiversity because metagenomic assembly fails to resolve microvariants among closely related genotypes. Aiming at understanding the universal factors that drive or constrain prokaryotic genome diversification, we performed an ecosystem-wide high-resolution metagenomic exploration of microdiversity by combining spatiotemporal (2 depths × 12 months) sampling from a pelagic freshwater system, high-quality MAG reconstruction using long- and short-read metagenomic sequences, and profiling of single nucleotide variants (SNVs) and structural variants (SVs) through mapping of short and long reads to the MAGs, respectively. We reconstructed 575 MAGs, including 29 circular assemblies, providing high-quality reference genomes of freshwater bacterioplankton. Read mapping against these MAGs identified 100 to 101,781 SNVs/Mb and 0 to 305 insertions, 0 to 467 deletions, 0 to 41 duplications, and 0 to 6 inversions for each MAG. Nonsynonymous SNVs were accumulated in genes potentially involved in cell surface structural modification to evade phage recognition. Most (80.2%) deletions overlapped with a gene coding region, and genes of prokaryotic defense systems were most frequently (>8% of the genes) overlapped with a deletion. Some such deletions exhibited a monthly shift in their allele frequency, suggesting a rapid turnover of genotypes in response to phage predation. MAGs with extremely low microdiversity were either rare or opportunistic bloomers, suggesting that population persistency is key to their genomic diversification. The results concluded that prokaryotic genomic diversification is driven primarily by viral load and constrained by a population bottleneck. IMPORTANCE Identifying intraspecies genomic diversity (microdiversity) is crucial to understanding microbial ecology and evolution. However, microdiversity among environmental assemblages is not well investigated, because most microbes are difficult to culture. In this study, we performed cultivation-independent exploration of bacterial genomic microdiversity in a lake ecosystem using a combination of short- and long-read metagenomic analyses. The results revealed the broad spectrum of genomic microdiversity among the diverse bacterial species in the ecosystem, which has been overlooked by conventional approaches. Our ecosystem-wide exploration further allowed comparative analysis among the genomes and genes and revealed factors behind microbial genomic diversification, namely, that diversification is driven primarily by resistance against viral infection and constrained by the population size.

Nanopore quality score resolution can be reduced with little effect on downstream analysis

Motivation

The use of high precision for representing quality scores in nanopore sequencing data makes these scores hard to compress and, thus, responsible for most of the information stored in losslessly compressed FASTQ files. This motivates the investigation of the effect of quality score information loss on downstream analysis from nanopore sequencing FASTQ files.

Results

We polished de novo assemblies for a mock microbial community and a human genome, and we called variants on a human genome. We repeated these experiments using various pipelines, under various coverage level scenarios and various quality score quantizers. In all cases, we found that the quantization of quality scores causes little difference (or even sometimes improves) on the results obtained with the original (non-quantized) data. This suggests that the precision that is currently used for nanopore quality scores may be unnecessarily high, and motivates the use of lossy compression algorithms for this kind of data. Moreover, we show that even a non-specialized compressor, such as gzip, yields large storage space savings after the quantization of quality scores.

Availability and supplementary information

Quantizers are freely available for download at: https://github.com/mrivarauy/QS-Quantizer.