Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing

Noyes, Noelle; Weinroth, Margaret D.; Parker, Jennifer; Dean, Chris; Lakin, Steven M.; Raymond, Robert; Rovira, Pablo; Doster, Enrique; Abdo, Zaid; Martin, J. N.; Jones, Kenneth L.; Ruiz, Jaime; Boucher, Christina; Belk, K. E.; Morley, Paul S.

doi:10.1186/s40168-017-0361-8

Cited by 61 publications

(90 citation statements)

References 58 publications

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“…The functional predictions generated for the species pan-genomes could also be leveraged to develop new culturing strategies for isolation of candidate species. Target-enrichment methods such as single-cell 39 and/or bait-capture hybridization 40 approaches could also be applied. Being able to enrich for specific groups of interest, even without culturing, could allow recovery of better-quality versions of MAGs and improve the analysis derived from genome sequence data alone.…”

Section: Discussionmentioning

confidence: 99%

A unified sequence catalogue of over 280,000 genomes obtained from the human gut microbiome

Almeida

Nayfach

Boland

et al. 2019

Preprint

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26Comprehensive reference data is essential for accurate taxonomic and functional 27 characterization of the human gut microbiome. Here we present the Unified Human 28Gastrointestinal Genome (UHGG) collection, a resource combining 286,997 genomes 29 representing 4,644 prokaryotic species from the human gut. These genomes contain over 625 30 million protein sequences used to generate the Unified Human Gastrointestinal Protein 31 (UHGP) catalogue, a collection that more than doubles the number of gut protein clusters over 32 the Integrated Gene Catalogue. We find that a large portion of the human gut microbiome 33 remains to be fully explored, with over 70% of the UHGG species lacking cultured 34 representatives, and 40% of the UHGP missing meaningful functional annotations. Intra-35 species genomic variation analyses revealed a large reservoir of accessory genes and single-36 nucleotide variants, many of which were specific to individual human populations. These freely 37 available genomic resources should greatly facilitate investigations into the human gut 38 microbiome. 39 3 Main 40 The human gut microbiome has been implicated in important phenotypes related to human 41 health and disease 1,2 . However, incomplete reference data that are missing microbial diversity 3 42 hamper our understanding of the roles of individual microbiome species, their interactions and 43 functions. Hence, establishing a comprehensive collection of microbial reference genomes and 44 genes is an important step for accurate characterization of the taxonomic and functional 45 repertoire of the intestinal microbial ecosystem. 46 47The Human Microbiome Project (HMP) 4 was a pioneering initiative to enrich our knowledge ( Supplementary Fig. 2), a standardized taxonomic framework based on a concatenated protein 112 phylogeny representing >140,000 public prokaryote genomes, fully resolved to the species 113 level (see 'Methods' for details on the taxonomy nomenclature used). However, over 60% of 114 6 the gut genomes could not be assigned to an existing species, confirming the majority of the 115 UHGG species lack representation in current reference databases. 116 117 Comparison of species recovered in individual studies 118We investigated how many of the 4,644 gut species were found in the different study 119 collections in order to determine their level of overlap and reproducibility, as well as the ratio 120 between cultured and uncultured species (Fig. 2a). The largest intersection was between the 121 collections of MAGs, with the same 1,081 species detected independently in the CIBIO, EBI 122 and HGM datasets, but not in any of the cultured genome studies. By restricting the analysis to 123 genomes recovered from 1,554 samples common to all three MAG studies, we found that 93-124 97% of species from each set were detected in at least one other MAG collection, and 79-86% 125 across all three ( Supplementary Fig. 3a). Similar level of species overlap was observed when 126 comparing studies on a per-sample basis ( Supplementary Fig. 3b). F...

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Section: Discussionmentioning

confidence: 99%

A unified sequence catalogue of over 280,000 genomes obtained from the human gut microbiome

Almeida

Nayfach

Boland

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Target capture is applied to the identification of the species composition in a community by designing baits for markers such as 16S or 18S rRNA genes (e.g., Cariou et al, 2018;Gasc and Peyret, 2018). The technique is also useful for the identification of prokaryote species based on their ecological function, by targeting gene families or functional DNA (e.g., Kushwaha et al, 2015;Noyes et al, 2017). For these applications, designing bait sets for prokaryotes involves similar considerations as those to be made for eukaryotes.…”

Section: Baits For Prokaryotesmentioning

confidence: 99%

A Guide to Carrying Out a Phylogenomic Target Sequence Capture Project

et al. 2020

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High-throughput DNA sequencing techniques enable time-and cost-effective sequencing of large portions of the genome. Instead of sequencing and annotating whole genomes, many phylogenetic studies focus sequencing effort on large sets of pre-selected loci, which further reduces costs and bioinformatic challenges while increasing coverage. One common approach that enriches loci before sequencing is often referred to as target sequence capture. This technique has been shown to be applicable to phylogenetic studies of greatly varying evolutionary depth. Moreover, it has proven to produce powerful, large multi-locus DNA sequence datasets suitable for phylogenetic analyses. However, target capture requires careful considerations, which may greatly affect the success of experiments. Here we provide a simple flowchart for designing phylogenomic target capture experiments. We discuss necessary decisions from the identification of target loci to the final bioinformatic processing of sequence data. We outline challenges and solutions related to the taxonomic scope, sample quality, and available genomic resources of target capture projects. We hope this review will serve as a useful roadmap for designing and carrying out successful phylogenetic target capture studies.

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“…For example, 47.7% of the reads mapped to the "OXA beta-lactamase" family could not be assigned to a specific allele (4,466 out of 9,357 reads; third-most abundant gene family by reads). Similarly, the most abundant gene family by reads in pig caeca was "tetracycline-resistant ribosomal protection protein", and 35.8% of the reads that mapped within this family could not be assigned to a specific allele (18,228 out of the 50,886 reads).…”

Section: Specific Mapping To Amr Genes and Allelic Variantsmentioning

confidence: 99%

The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples

Gweon

Shaw

Swann

et al. 2019

Preprint

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on behalf of the REHAB consortium. ABSTRACTBackground. Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short-and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an opensource software pipeline, 'ResPipe'. Results.Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve <1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially Gweon et al. 3 changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment.Conclusions. Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available (https://gitlab.com/hsgweon/ResPipe).

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Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing

Cited by 61 publications

References 58 publications

A unified sequence catalogue of over 280,000 genomes obtained from the human gut microbiome

A unified sequence catalogue of over 280,000 genomes obtained from the human gut microbiome

A Guide to Carrying Out a Phylogenomic Target Sequence Capture Project

The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples

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