Identification of Microbial Dark Matter in Antarctic Environments
Numerous studies have applied molecular techniques to understand the diversity, evolution, and ecological function of Antarctic bacteria and archaea. One common technique is sequencing of the 16S rRNA gene, which produces a nearly quantitative profile of community membership. However, the utility of...
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ftcdlib:qt0jp9010f 2023-05-15T14:03:27+02:00 Identification of Microbial Dark Matter in Antarctic Environments Bowman, Jeff S 3165 2018-12-19 application/pdf http://www.escholarship.org/uc/item/0jp9010f english eng eScholarship, University of California qt0jp9010f http://www.escholarship.org/uc/item/0jp9010f public Bowman, Jeff S. (2018). Identification of Microbial Dark Matter in Antarctic Environments. FRONTIERS IN MICROBIOLOGY, 9, 3165. doi:10.3389/fmicb.2018.03165. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/0jp9010f Antarctica 16S rRNA glacier sea ice cryoconite sediment permafrost snow article 2018 ftcdlib https://doi.org/10.3389/fmicb.2018.03165 2019-01-25T23:52:33Z Numerous studies have applied molecular techniques to understand the diversity, evolution, and ecological function of Antarctic bacteria and archaea. One common technique is sequencing of the 16S rRNA gene, which produces a nearly quantitative profile of community membership. However, the utility of this and similar approaches is limited by what is known about the evolution, physiology, and ecology of surveyed taxa. When representative genomes are available in public databases some of this information can be gleaned from genomic studies, and automated pipelines exist to carry out this task. Here the paprica metabolic inference pipeline was used to assess how well Antarctic microbial communities are represented by the available completed genomes. The NCBI's Sequence Read Archive (SRA) was searched for Antarctic datasets that used one of the Illumina platforms to sequence the 16S rRNA gene. These data were quality controlled and denoised to identify unique reads, then analyzed with paprica to determine the degree of overlap with the closest phylogenetic neighbor with a completely sequenced genome. While some unique reads had perfect mapping to 16S rRNA genes from completed genomes, the mean percent overlap for all mapped reads was 86.6. When samples were grouped by environment, some environments appeared more or less well represented by the available genomes. For the domain Bacteria, seawater was particularly poorly represented with a mean overlap of 80.2, while for the domain Archaea glacial ice was particularly poorly represented with an overlap of only 48.0 for a single sample. These findings suggest that a considerable effort is needed to improve the representation of Antarctic microbes in genome sequence databases. Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice permafrost Sea ice University of California: eScholarship Antarctic Frontiers in Microbiology 9 |
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Open Polar |
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University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
English |
topic |
Antarctica 16S rRNA glacier sea ice cryoconite sediment permafrost snow |
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Antarctica 16S rRNA glacier sea ice cryoconite sediment permafrost snow Bowman, Jeff S Identification of Microbial Dark Matter in Antarctic Environments |
topic_facet |
Antarctica 16S rRNA glacier sea ice cryoconite sediment permafrost snow |
description |
Numerous studies have applied molecular techniques to understand the diversity, evolution, and ecological function of Antarctic bacteria and archaea. One common technique is sequencing of the 16S rRNA gene, which produces a nearly quantitative profile of community membership. However, the utility of this and similar approaches is limited by what is known about the evolution, physiology, and ecology of surveyed taxa. When representative genomes are available in public databases some of this information can be gleaned from genomic studies, and automated pipelines exist to carry out this task. Here the paprica metabolic inference pipeline was used to assess how well Antarctic microbial communities are represented by the available completed genomes. The NCBI's Sequence Read Archive (SRA) was searched for Antarctic datasets that used one of the Illumina platforms to sequence the 16S rRNA gene. These data were quality controlled and denoised to identify unique reads, then analyzed with paprica to determine the degree of overlap with the closest phylogenetic neighbor with a completely sequenced genome. While some unique reads had perfect mapping to 16S rRNA genes from completed genomes, the mean percent overlap for all mapped reads was 86.6. When samples were grouped by environment, some environments appeared more or less well represented by the available genomes. For the domain Bacteria, seawater was particularly poorly represented with a mean overlap of 80.2, while for the domain Archaea glacial ice was particularly poorly represented with an overlap of only 48.0 for a single sample. These findings suggest that a considerable effort is needed to improve the representation of Antarctic microbes in genome sequence databases. |
format |
Article in Journal/Newspaper |
author |
Bowman, Jeff S |
author_facet |
Bowman, Jeff S |
author_sort |
Bowman, Jeff S |
title |
Identification of Microbial Dark Matter in Antarctic Environments |
title_short |
Identification of Microbial Dark Matter in Antarctic Environments |
title_full |
Identification of Microbial Dark Matter in Antarctic Environments |
title_fullStr |
Identification of Microbial Dark Matter in Antarctic Environments |
title_full_unstemmed |
Identification of Microbial Dark Matter in Antarctic Environments |
title_sort |
identification of microbial dark matter in antarctic environments |
publisher |
eScholarship, University of California |
publishDate |
2018 |
url |
http://www.escholarship.org/uc/item/0jp9010f |
op_coverage |
3165 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Antarctica Ice permafrost Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Ice permafrost Sea ice |
op_source |
Bowman, Jeff S. (2018). Identification of Microbial Dark Matter in Antarctic Environments. FRONTIERS IN MICROBIOLOGY, 9, 3165. doi:10.3389/fmicb.2018.03165. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/0jp9010f |
op_relation |
qt0jp9010f http://www.escholarship.org/uc/item/0jp9010f |
op_rights |
public |
op_doi |
https://doi.org/10.3389/fmicb.2018.03165 |
container_title |
Frontiers in Microbiology |
container_volume |
9 |
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1766274117769625600 |