biostudies-literatureChaumeil PAUQ Strategic Funding and Australian Research Council Laureate Fellowship5315-5316https://www.ebi.ac.uk/biostudies/studies/S-EPMC9710552biostudies-literatureUnknown38(23)<h4>Summary</h4>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.<h4>Availability and implementation</h4>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.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.Bioinformatics (Oxford, England)GTDB-Tk v2: memory friendly classification with the genome taxonomy database.PMC9710552FL150100038Parks DHMussig AJHugenholtz PChaumeil PAfalseGTDB-Tk v2: memory friendly classification with the genome taxonomy database.<h4>Summary</h4>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.<h4>Availability and implementation</h4>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.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.2022-01-01T00:00:00Z2022 Nov2026-05-05T02:50:50.285Z2025-04-06T13:59:20.466ZS-EPMC97105523621846310.1093/bioinformatics/btac672