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

Autor: Dimopoulos EA; The Palaeogenomics and Bio-archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, United Kingdom., Carmagnini A; Palaeogenomics Group, Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, Ludwig Maximilian University, Munich, Germany.; School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom., Velsko IM; Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Jena, Germany., Warinner C; Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Jena, Germany.; Department of Anthropology, Harvard University, Cambridge, United States of America., Larson G; The Palaeogenomics and Bio-archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, United Kingdom., Frantz LAF; Palaeogenomics Group, Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, Ludwig Maximilian University, Munich, Germany.; School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom., Irving-Pease EK; The Palaeogenomics and Bio-archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, United Kingdom.; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2022 Sep 30; Vol. 18 (9), pp. e1010493. Date of Electronic Publication: 2022 Sep 30 (Print Publication: 2022).
DOI: 10.1371/journal.pcbi.1010493
Abstrakt: 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 read 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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