GTDB-Tk

GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy (GTDB). It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes. The GTDB-Tk is open source and released under the GNU General Public License (Version 3).

Notifications about GTDB-Tk releases will be available through the GTDB Twitter account and the GTDB Announcements Forum.

Please post questions and issues related to GTDB-Tk on the Issues section of the GitHub repository. Questions related to the GTDB can be posted on the GTDB Forum or sent to the GTDB team.

🚀 Getting started

Be sure to check the hardware requirements, then choose your preferred method:

• Bioconda
• Docker
• pip

📖 Documentation

Documentation for GTDB-Tk can be found here.

✨ New Features

GTDB-Tk v2.7.0+ includes the following new features:

• Pre-sketched skani database: GTDB-Tk now uses a skani pre-sketched database of the GTDB representative genomes. This significantly reduces the database storage footprint from 198 GB (in Release 232) down to 98 GB.
• Representative genomes availability: The GTDB representative genomes are now available via the "Download" page on the GTDB website.
• Deprecated flag: Because the database is already sketched natively, the --skani_sketch_dir flag is now deprecated.
• Replaced --skip_ani_screen with --place_species: The --skip_ani_screen flag is now deprecated in v2.7.0 and has been replaced by the --place_species flag. The logic has been updated to reflect the new database structure:
  • Previously: Using --skip_ani_screen, genomes placed in a genus by pplacer were only compared to representative genomes within that specific genus.
  • Now: Because the database is a single skani sketch, user genomes are compared against all GTDB reference genomes once at the very beginning of the pipeline. When the new --place_species flag is selected, the genomes are still explicitly placed in the reference tree.

⚠️ IMPORTANT MEMORY WARNING: The divide-and-conquer approach now requires more than 128 GB of RAM. Specifically, you will need at least 140 GB of RAM for R232.

📈 Performance

Using ANI screen "can" reduce computation by >50%, although it depends on the set of input genomes. A set of input genomes consisting primarily of new species will not benefit from ANI screen as much as a set of genomes that are largely assigned to GTDB species clusters. In the latter case, the ANI screen will reduce the number of genomes that need to be classified by pplacer which reduces computation time substantially (between 25% and 60% in our testing).

📚 References

GTDB-Tk is described in:

• Chaumeil PA, et al. 2022. GTDB-Tk v2: memory friendly classification with the Genome Taxonomy Database. Bioinformatics, btac672.
• Chaumeil PA, et al. 2019. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics, btz848.

The Genome Taxonomy Database (GTDB) is described in:

• Parks, D.H., et al. (2021). GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research, 50: D785–D794.
• Rinke, C, et al. (2021). A standardized archaeal taxonomy for the Genome Taxonomy Database. Nature Microbiology, 6: 946–959.
• Parks, D.H., et al. 2020. A complete domain-to-species taxonomy for Bacteria and Archaea. Nature Biotechnology, https://doi.org/10.1038/s41587-020-0501-8.
• Parks DH, et al. 2018. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology, http://dx.doi.org/10.1038/nbt.4229.

We strongly encourage you to cite the following 3rd party dependencies:

• Matsen FA, et al. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics, 11:538.
• Shaw J. and Yu Y.W. 2023. Fast and robust metagenomic sequence comparison through sparse chaining with skani. Nature Methods, 20, pages1661–1665 (2023).
• Hyatt D, et al. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 11:119. doi: 10.1186/1471-2105-11-119.
• Price MN, et al. 2010. FastTree 2 - Approximately Maximum-Likelihood Trees for Large Alignments. PLoS One, 5, e9490.
• Eddy SR. 2011. Accelerated profile HMM searches. PLOS Comp. Biol., 7:e1002195.

© Copyright

Copyright 2017 Pierre-Alain Chaumeil. See LICENSE for further details.