Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes

Zhou, Zhemin; Luhmann, Nina; Alikhan, Nabil-Fareed; Quince, Christopher; Achtman, Mark

doi:10.1101/215707

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Tools for detecting microbial species from metagenomes include metagenomic classifiers that report taxonomic relative abundances [ 21 ], such as Kraken2 [ 22 , 23 ], KrakenUniq [ 24 ] and the Megan Alignment Tool (MALT) [ 25 ], as well as species identification pipelines that classify individual reads, such as Sigma [ 3 ], Strain Prediction and Analysis using Representative Sequences (SPARSE) [ 26 ], Bayesian Reestimation of Abundance with KrakEN (Bracken) [ 27 ], and Heuristic Operations for Pathogen Screening (HOPS, specifically designed for ancient DNA) [ 28 ]. The metagenomic classifiers Kraken2, KrakenUniq and MALT provide a good starting point for exploratory studies, especially when it is not known which pathogens may be present [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…These tools, however, can be slow, require a large amount of RAM [ 24 , 25 , 28 ], and are prone to false positive identifications of low abundance species [ 30 ]. The species identification pipelines Sigma [ 3 ] and SPARSE [ 26 ] are based on more sophisticated statistical models but they require alignment to a large database, which can be slow [ 26 ], and do not provide statistical support for individual species identifications.…”

Section: Introductionmentioning

confidence: 99%

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HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

et al. 2022

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Identification of specific species in metagenomic samples is critical for several key applications, yet many tools available require large computational power and are often prone to false positive identifications. Here we describe High-AccuracY and Scalable Taxonomic Assignment of MetagenomiC data (HAYSTAC), which can estimate the probability that a specific taxon is present in a metagenome. HAYSTAC provides a user-friendly tool to construct databases, based on publicly available genomes, that are used for competitive reads mapping. It then uses a novel Bayesian framework to infer the abundance and statistical support for each species identification and provide per-read species classification. Unlike other methods, HAYSTAC is specifically designed to efficiently handle both ancient and modern DNA data, as well as incomplete reference databases, making it possible to run highly accurate hypothesis-driven analyses (i.e., assessing the presence of a specific species) on variably sized reference databases while dramatically improving processing speeds. We tested the performance and accuracy of HAYSTAC using simulated Illumina libraries, both with and without ancient DNA damage, and compared the results to other currently available methods (i.e., Kraken2/Bracken, KrakenUniq, MALT/HOPS, and Sigma). HAYSTAC identified fewer false positives than both Kraken2/Bracken, KrakenUniq and MALT in all simulations, and fewer than Sigma in simulations of ancient data. It uses less memory than Kraken2/Bracken, KrakenUniq as well as MALT both during database construction and sample analysis. Lastly, we used HAYSTAC to search for specific pathogens in two published ancient metagenomic datasets, demonstrating how it can be applied to empirical datasets. HAYSTAC is available from https://github.com/antonisdim/HAYSTAC.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

et al. 2022

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Metagenomics Strain Resolution on Assembly Graphs

Quince

Nurk

Raguideau

et al. 2020

Preprint

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We introduce a novel bioinformatics pipeline, STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, when multiple metagenome samples from the same community are available. STRONG performs coassembly, followed by binning into metagenome assembled genomes (MAGs), but uniquely it stores the coassembly graph prior to simplification of variants. This enables the subgraphs for individual single-copy core genes (SCGs) in each MAG to be extracted. It can then thread back reads from the samples to compute per sample coverages for the unitigs in these graphs. These graphs and their unitig coverages are then used in a Bayesian algorithm, BayesPaths, that determines the number of strains present, their sequences or haplotypes on the SCGs and their abundances in each of the samples. Our approach both avoids the ambiguities of read mapping and allows more of the information on co-occurrence of variants in reads to be utilised than if variants were treated independently, whilst at the same time exploiting the correlation of variants across samples that occurs when they are linked in the same strain. We compare STRONG to the current state of the art on synthetic communities and demonstrate that we can recover more strains, more accurately, and with a realistic estimate of uncertainty deriving from the variational Bayesian algorithm employed for the strain resolution. On a real anaerobic digestor time series we obtained strain-resolved SCGs for over 300 MAGs that for abundant community members match those observed from long Nanopore reads.

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A quantitative metagenomic approach to determine population concentrations with examination of quantitative limitations

Langenfeld

Hegarty

Vidaurri

et al. 2022

Preprint

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Quantitative metagenomic methods are maturing but continue to lack clearly-defined analytical limits. We used synthetic ssDNA and dsDNA standards to develop a quantitative metagenomic approach, called QuantMeta, that accounts for detection and quantification limitations. QuantMeta applies entropy-based detection limits that incorporate both read distribution and coverage and sets read depth variability thresholds to detect and correct quantification errors caused by non-specific mapping and assembly errors. In proof-of-concept wastewater metagenomes, we compared QuantMeta-based total virus concentrations, as well as polyomaviruses and crAss-like phages of interest, to those of traditional, low-throughput methods. The QuantMeta approach, applicable to both viral and cellular metagenomes, advances quantitative metagenomics by improving the accuracy of measured target concentrations.

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Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes

Cited by 4 publications

References 24 publications

HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

Metagenomics Strain Resolution on Assembly Graphs

A quantitative metagenomic approach to determine population concentrations with examination of quantitative limitations

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