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aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow
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 dem...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591440/ https://www.ncbi.nlm.nih.gov/pubmed/37872569 http://dx.doi.org/10.1186/s13059-023-03083-9 |
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author | Pochon, Zoé Bergfeldt, Nora Kırdök, Emrah Vicente, Mário Naidoo, Thijessen van der Valk, Tom Altınışık, N. Ezgi Krzewińska, Maja Dalén, Love Götherström, Anders Mirabello, Claudio Unneberg, Per Oskolkov, Nikolay |
author_facet | Pochon, Zoé Bergfeldt, Nora Kırdök, Emrah Vicente, Mário Naidoo, Thijessen van der Valk, Tom Altınışık, N. Ezgi Krzewińska, Maja Dalén, Love Götherström, Anders Mirabello, Claudio Unneberg, Per Oskolkov, Nikolay |
author_sort | Pochon, Zoé |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03083-9. |
format | Online Article Text |
id | pubmed-10591440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105914402023-10-24 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 Götherström, Anders Mirabello, Claudio Unneberg, Per Oskolkov, Nikolay Genome Biol Method 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03083-9. BioMed Central 2023-10-23 /pmc/articles/PMC10591440/ /pubmed/37872569 http://dx.doi.org/10.1186/s13059-023-03083-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Method Pochon, Zoé Bergfeldt, Nora Kırdök, Emrah Vicente, Mário Naidoo, Thijessen van der Valk, Tom Altınışık, N. Ezgi Krzewińska, Maja Dalén, Love Götherström, Anders Mirabello, Claudio Unneberg, Per Oskolkov, Nikolay aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow |
title | aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow |
title_full | aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow |
title_fullStr | aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow |
title_full_unstemmed | aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow |
title_short | aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow |
title_sort | ameta: an accurate and memory-efficient ancient metagenomic profiling workflow |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591440/ https://www.ncbi.nlm.nih.gov/pubmed/37872569 http://dx.doi.org/10.1186/s13059-023-03083-9 |
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