High-resolution shotgun metagenomics: the more data, the better?
Abstract In shotgun metagenomics (SM), the state-of-the-art bioinformatic workflows are referred to as high-resolution shotgun metagenomics (HRSM) and require intensive computing and disk storage resources. While the increase in data output of the latest iteration of high-throughput DNA sequencing s...
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croxfordunivpr:10.1093/bib/bbac443 2024-09-30T14:23:53+00:00 High-resolution shotgun metagenomics: the more data, the better? Tremblay, Julien Schreiber, Lars Greer, Charles W 2022 http://dx.doi.org/10.1093/bib/bbac443 https://academic.oup.com/bib/article-pdf/23/6/bbac443/47244300/bbac443.pdf en eng Oxford University Press (OUP) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model Briefings in Bioinformatics volume 23, issue 6 ISSN 1467-5463 1477-4054 journal-article 2022 croxfordunivpr https://doi.org/10.1093/bib/bbac443 2024-09-03T04:11:28Z Abstract In shotgun metagenomics (SM), the state-of-the-art bioinformatic workflows are referred to as high-resolution shotgun metagenomics (HRSM) and require intensive computing and disk storage resources. While the increase in data output of the latest iteration of high-throughput DNA sequencing systems can allow for unprecedented sequencing depth at a minimal cost, adjustments in HRSM workflows will be needed to properly process these ever-increasing sequence datasets. One potential adaptation is to generate so-called shallow SM datasets that contain fewer sequencing data per sample as compared with the more classic high coverage sequencing. While shallow sequencing is a promising avenue for SM data analysis, detailed benchmarks using real-data are lacking. In this case study, we took four public SM datasets, one massive and the others moderate in size and subsampled each dataset at various levels to mimic shallow sequencing datasets of various sequencing depths. Our results suggest that shallow SM sequencing is a viable avenue to obtain sound results regarding microbial community structures and that high-depth sequencing does not bring additional elements for ecological interpretation. More specifically, results obtained by subsampling as little as 0.5 M sequencing clusters per sample were similar to the results obtained with the largest subsampled dataset for human gut and agricultural soil datasets. For an Antarctic dataset, which contained only a few samples, 4 M sequencing clusters per sample was found to generate comparable results to the full dataset. One area where ultra-deep sequencing and maximizing the usage of all data was undeniably beneficial was in the generation of metagenome-assembled genomes. Article in Journal/Newspaper Antarc* Antarctic Oxford University Press Antarctic Briefings in Bioinformatics 23 6 |
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Oxford University Press |
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croxfordunivpr |
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English |
description |
Abstract In shotgun metagenomics (SM), the state-of-the-art bioinformatic workflows are referred to as high-resolution shotgun metagenomics (HRSM) and require intensive computing and disk storage resources. While the increase in data output of the latest iteration of high-throughput DNA sequencing systems can allow for unprecedented sequencing depth at a minimal cost, adjustments in HRSM workflows will be needed to properly process these ever-increasing sequence datasets. One potential adaptation is to generate so-called shallow SM datasets that contain fewer sequencing data per sample as compared with the more classic high coverage sequencing. While shallow sequencing is a promising avenue for SM data analysis, detailed benchmarks using real-data are lacking. In this case study, we took four public SM datasets, one massive and the others moderate in size and subsampled each dataset at various levels to mimic shallow sequencing datasets of various sequencing depths. Our results suggest that shallow SM sequencing is a viable avenue to obtain sound results regarding microbial community structures and that high-depth sequencing does not bring additional elements for ecological interpretation. More specifically, results obtained by subsampling as little as 0.5 M sequencing clusters per sample were similar to the results obtained with the largest subsampled dataset for human gut and agricultural soil datasets. For an Antarctic dataset, which contained only a few samples, 4 M sequencing clusters per sample was found to generate comparable results to the full dataset. One area where ultra-deep sequencing and maximizing the usage of all data was undeniably beneficial was in the generation of metagenome-assembled genomes. |
format |
Article in Journal/Newspaper |
author |
Tremblay, Julien Schreiber, Lars Greer, Charles W |
spellingShingle |
Tremblay, Julien Schreiber, Lars Greer, Charles W High-resolution shotgun metagenomics: the more data, the better? |
author_facet |
Tremblay, Julien Schreiber, Lars Greer, Charles W |
author_sort |
Tremblay, Julien |
title |
High-resolution shotgun metagenomics: the more data, the better? |
title_short |
High-resolution shotgun metagenomics: the more data, the better? |
title_full |
High-resolution shotgun metagenomics: the more data, the better? |
title_fullStr |
High-resolution shotgun metagenomics: the more data, the better? |
title_full_unstemmed |
High-resolution shotgun metagenomics: the more data, the better? |
title_sort |
high-resolution shotgun metagenomics: the more data, the better? |
publisher |
Oxford University Press (OUP) |
publishDate |
2022 |
url |
http://dx.doi.org/10.1093/bib/bbac443 https://academic.oup.com/bib/article-pdf/23/6/bbac443/47244300/bbac443.pdf |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
Briefings in Bioinformatics volume 23, issue 6 ISSN 1467-5463 1477-4054 |
op_rights |
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
op_doi |
https://doi.org/10.1093/bib/bbac443 |
container_title |
Briefings in Bioinformatics |
container_volume |
23 |
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6 |
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1811638905070419968 |