Oxford Nanopore R10.4 long-read sequencing enables near-perfect bacterial genomes from pure cultures and metagenomes without short-read or reference polishing

Sereika, Mantas; Kirkegaard, Rasmus Hansen; Karst, Søren Michael; Michaelsen, Thomas Yssing; Sørensen, Emil A.; Wollenberg, Rasmus Dam; Albertsen, Mads

doi:10.1101/2021.10.27.466057

Cited by 34 publications

(40 citation statements)

References 89 publications

(99 reference statements)

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“…In particular, even higher-quality metagenomic assemblies are possible if HiFi reads from the Pacific Biosciences Sequel IIe system are available 46 . Also, the recent announcement of higher-quality reads from ONT could help improve assembly further and reduce costs 47 . Even as the sequencing landscape is constantly changing, the results from our study suggest that high-quality population-specific metagenomic references are already feasible with a modest-sized cohort and limited sequencing resources.…”

Section: Discussionmentioning

confidence: 99%

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Emmanuel

Chia

Bertrand

et al. 2022

Preprint

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Despite extensive efforts to address it, the vastness of uncharacterized "dark matter" microbial genetic diversity can impact short-read sequencing based metagenomic studies. Population specific biases in genomic reference databases can further compound this problem. Leveraging advances in long-read and Hi-C technologies, we deeply characterized 109 gut microbiomes from three ethnicities in Singapore to comprehensively reconstruct 4,497 medium and high-quality metagenome assembled genomes, 1,708 of which were missing in short-read only analysis and with >28x N50 improvement. Species-level clustering identified 70 (>10% of total) novel gut species out of 685, improved reference genomes for 363 species (53% of total), and discovered 3,413 strains that are unique to these populations. Among the top 10 most abundant gut bacteria in our study, one of the species and >80% of all strains were not represented in existing databases. Annotation of biosynthetic gene clusters (BGCs) uncovered more than 27,000 BGCs with a large fraction (36-88%) not represented in current databases, and with several unique clusters predicted to produce bacteriocins that could significantly alter microbiome community structure. These results reveal the significant uncharacterized gut microbial diversity in Southeast Asian populations and highlight the utility of hybrid metagenomic references for bioprospecting and disease-focused studies.

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Section: Discussionmentioning

confidence: 99%

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Emmanuel

Chia

Bertrand

et al. 2022

Preprint

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“…Recently introduced ONT chemistry (R10.4) than the evaluated version has been reported to generate near-perfect bacterial genome assemblies using ONT reads alone (37). In particular, R10.4 showed a substantial improvement over the evaluated version (R9) in resolving homopolymer sizes, leading to correct size determination for the majority of homopolymers up to 10 bps (37). While this ability may allow HEC-free cgMLST using ONT reads, HEC can provide forward compatibility for isolates that have been or continue to be sequenced by the R9 chemistry to facilitate their co-analysis with future ONT-sequenced genomes presumably not requiring HEC.…”

Section: Entericamentioning

confidence: 99%

Subtyping evaluation of Salmonella Enteritidis using SNP and core genome MLST with nanopore reads

Xian

Mann

et al. 2022

Preprint

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Whole genome sequencing (WGS) for public health surveillance and epidemiological investigation of foodborne pathogens predominantly relies on sequencing platforms that generate short reads. Continuous improvement of long-read nanopore sequencing such as Oxford Nanopore Technologies (ONT) presents a potential for leveraging multiple advantages of the technology in public health and food industry settings, including rapid turnaround and onsite applicability in addition to superior read length. However, evaluation, standardization and implementation of the ONT approach to WGS-based, strain-level subtyping is challenging, in part due to its relatively high base-calling error rates and frequent iterations of sequencing chemistry and bioinformatic analytics. Using an established cohort of Salmonella Enteritidis isolates for subtyping evaluation, we assessed the technical readiness of ONT for single nucleotide polymorphism (SNP) analysis and core-genome multilocus sequence typing (cgMLST) of a major foodborne pathogen. By multiplexing three isolates per flow cell, we generated sufficient sequencing depths under seven hours of sequencing for robust subtyping. SNP calls by ONT and Illumina reads were highly concordant despite homopolymer errors in ONT reads (R9.4.1 chemistry). In silico correction of such errors allowed accurate allelic calling for cgMLST and allelic difference measurements to facilitate heuristic detection of outbreak isolates. Our study established a baseline for the continuously evolving nanopore technology as a viable solution to high quality subtyping of Salmonella, delivering comparable subtyping performance when used standalone or together with short-read platforms.

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“…Finally, most binning methods are developed only on short-read assemblies [5], and only very few binners have been developed with a focus on long-read assemblies [18, 23]. While long-read sequencing technologies have gained traction, there is still a lack of benchmarks and studies on long-read sequencing for metagenomics [24, 25, 26, 27]. The longer read length results in much improved assemblies that generates more sparse assembly graphs and enables more robust estimations of composition and coverage.…”

Section: Introductionmentioning

confidence: 99%

Metagenomic binning with assembly graph embeddings

Lamurias

Sereika

Albertsen

et al. 2022

Preprint

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Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning. We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared to state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning.

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Oxford Nanopore R10.4 long-read sequencing enables near-perfect bacterial genomes from pure cultures and metagenomes without short-read or reference polishing

Cited by 34 publications

References 89 publications

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Subtyping evaluation of Salmonella Enteritidis using SNP and core genome MLST with nanopore reads

Metagenomic binning with assembly graph embeddings

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