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

Pochon, Zoé ; Bergfeldt, Nora ; Kırdök, Emrah ; Vicente, Mário ; Naidoo, Thijessen ; van der Valk, Tom ; Altınışık, N. Ezgi ; Krzewińska, Maja ; Dalén, Love LU and Götherström, Anders , et al. (2023) In Genome Biology 24.
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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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Ancient DNA, Ancient metagenomics, Microbiome profiling, Pathogen detection
in
Genome Biology
volume
24
article number
242
pages
30 pages
publisher
BioMed Central (BMC)
external identifiers
  • scopus:85174716587
  • pmid:37872569
ISSN
1474-7596
DOI
10.1186/s13059-023-03083-9
language
English
LU publication?
yes
id
ac4a000b-898b-4b7a-bece-a682a7c0dfc4
date added to LUP
2023-12-11 12:51:52
date last changed
2024-12-19 07:47:07
@article{ac4a000b-898b-4b7a-bece-a682a7c0dfc4,
  abstract     = {{<p>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.</p>}},
  author       = {{Pochon, Zoé and Bergfeldt, Nora and Kırdök, Emrah and Vicente, Mário and Naidoo, Thijessen and van der Valk, Tom and Altınışık, N. Ezgi and Krzewińska, Maja and Dalén, Love and Götherström, Anders and Mirabello, Claudio and Unneberg, Per and Oskolkov, Nikolay}},
  issn         = {{1474-7596}},
  keywords     = {{Ancient DNA; Ancient metagenomics; Microbiome profiling; Pathogen detection}},
  language     = {{eng}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Genome Biology}},
  title        = {{aMeta : an accurate and memory-efficient ancient metagenomic profiling workflow}},
  url          = {{http://dx.doi.org/10.1186/s13059-023-03083-9}},
  doi          = {{10.1186/s13059-023-03083-9}},
  volume       = {{24}},
  year         = {{2023}},
}