aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow

Zoé Pochon; Nora Bergfeldt; Emrah Kırdök; Mário Vicente; Thijessen Naidoo; Tom van der Valk; N. Ezgi Altınışık; Maja Krzewińska; Love Dalén; Anders Götherström; Claudio Mirabello; Per Unneberg; Nikolay Oskolkov

doi:10.1186/s13059-023-03083-9

aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow

Zoé Pochon, Nora Bergfeldt, Emrah Kırdök, Mário Vicente, Thijessen Naidoo, Tom van der Valk, N. Ezgi Altınışık, Maja Krzewińska, Love Dalén, Anders Götherström, Claudio Mirabello, Per Unneberg, Nikolay Oskolkov

Research output: Contribution to journal › Article › peer-review

Abstract

Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

Original language	English
Article number	242
Number of pages	30
Journal	Genome Biology
Volume	24
DOIs	https://doi.org/10.1186/s13059-023-03083-9
Publication status	Published - 2023 Dec

Subject classification (UKÄ)

Bioinformatics (Computational Biology)

Free keywords

Ancient DNA
Ancient metagenomics
Microbiome profiling
Pathogen detection

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10.1186/s13059-023-03083-9

National Bioinformatics Infrastructure Sweden
Ahrén, D. (Manager), Ringnér, M. (Manager), Levander, F. (Manager), Manoharan, L. (Manager), Oskolkov, N. (Manager), Vasquez, L. J. A. (Manager), Pyl, P. T. (Manager), Agarwal, P. (Manager), Li, Y. (Manager), Volpe, M. (Manager) & Kozjek, K. (Manager)
Department of Biology
Infrastructure

Cite this

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title = "aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow",

abstract = "Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.",

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year = "2023",

month = dec,

doi = "10.1186/s13059-023-03083-9",

language = "English",

volume = "24",

journal = "Genome Biology",

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TY - JOUR

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T2 - an accurate and memory-efficient ancient metagenomic profiling workflow

AU - Pochon, Zoé

AU - Bergfeldt, Nora

AU - Kırdök, Emrah

AU - Vicente, Mário

AU - Naidoo, Thijessen

AU - van der Valk, Tom

AU - Altınışık, N. Ezgi

AU - Krzewińska, Maja

AU - Dalén, Love

AU - Götherström, Anders

AU - Mirabello, Claudio

AU - Unneberg, Per

AU - Oskolkov, Nikolay

PY - 2023/12

Y1 - 2023/12

N2 - Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

AB - Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

KW - Ancient DNA

KW - Ancient metagenomics

KW - Microbiome profiling

KW - Pathogen detection

U2 - 10.1186/s13059-023-03083-9

DO - 10.1186/s13059-023-03083-9

M3 - Article

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AN - SCOPUS:85174716587

SN - 1474-7596

VL - 24

JO - Genome Biology

JF - Genome Biology

M1 - 242

ER -

aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow

Abstract

Subject classification (UKÄ)

Free keywords

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National Bioinformatics Infrastructure Sweden

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