GTDB-Tk v2: memory friendly classification with the genome taxonomy database
The Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits...
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| Published in: | Bioinformatics (Oxford, England) Vol. 38; no. 23; pp. 5315 - 5316 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Oxford University Press
30.11.2022
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| Subjects: | |
| ISSN: | 1367-4803, 1367-4811, 1367-4811 |
| Online Access: | Get full text |
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| Abstract | The Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits its adoption and ease of use. Here, we present an update to GTDB-Tk that uses a divide-and-conquer approach where user genomes are initially placed into a bacterial reference tree with family-level representatives followed by placement into an appropriate class-level subtree comprising species representatives. This substantially reduces the memory requirements of GTDB-Tk while having minimal impact on classification.
GTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk.
Supplementary data are available at Bioinformatics online. |
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| AbstractList | The Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits its adoption and ease of use. Here, we present an update to GTDB-Tk that uses a divide-and-conquer approach where user genomes are initially placed into a bacterial reference tree with family-level representatives followed by placement into an appropriate class-level subtree comprising species representatives. This substantially reduces the memory requirements of GTDB-Tk while having minimal impact on classification.
GTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk.
Supplementary data are available at Bioinformatics online. The Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits its adoption and ease of use. Here, we present an update to GTDB-Tk that uses a divide-and-conquer approach where user genomes are initially placed into a bacterial reference tree with family-level representatives followed by placement into an appropriate class-level subtree comprising species representatives. This substantially reduces the memory requirements of GTDB-Tk while having minimal impact on classification.SUMMARYThe Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits its adoption and ease of use. Here, we present an update to GTDB-Tk that uses a divide-and-conquer approach where user genomes are initially placed into a bacterial reference tree with family-level representatives followed by placement into an appropriate class-level subtree comprising species representatives. This substantially reduces the memory requirements of GTDB-Tk while having minimal impact on classification.GTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk.AVAILABILITY AND IMPLEMENTATIONGTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. |
| Author | Mussig, Aaron J Hugenholtz, Philip Parks, Donovan H Chaumeil, Pierre-Alain |
| Author_xml | – sequence: 1 givenname: Pierre-Alain orcidid: 0000-0003-0426-8445 surname: Chaumeil fullname: Chaumeil, Pierre-Alain – sequence: 2 givenname: Aaron J orcidid: 0000-0002-9988-0866 surname: Mussig fullname: Mussig, Aaron J – sequence: 3 givenname: Philip surname: Hugenholtz fullname: Hugenholtz, Philip – sequence: 4 givenname: Donovan H orcidid: 0000-0001-6662-9010 surname: Parks fullname: Parks, Donovan H |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36218463$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1038/s41587-020-0718-6 10.1038/s41587-020-0501-8 10.1186/1471-2105-11-538 10.1038/s41587-020-0603-3 10.1111/1755-0998.13527 10.1093/sysbio/syy054 10.1093/bioinformatics/btz848 10.1093/nar/gkab776 |
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| References | Parks (2022113016193824100_btac672-B8) 2022; 50 Balaban (2022113016193824100_btac672-B2) 2022; 22 Almeida (2022113016193824100_btac672-B1) 2021; 39 Nayfach (2022113016193824100_btac672-B6) 2021; 39 Matsen (2022113016193824100_btac672-B5) 2010; 11 Parks (2022113016193824100_btac672-B7) 2020; 38 Barbera (2022113016193824100_btac672-B3) 2019; 68 Chaumeil (2022113016193824100_btac672-B4) 2019; 36 |
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