Note (30/08/2018) :
- A new version of the data (release 86) is available under this link.
- This new version is required to run GTDB-Tk v0.1.0+
Note (19/04/2018) :
- A new version of the data (release 83) is available under this link.
- This new version is recommended to run GTDB-Tk v0.0.6+
GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes. It is computationally efficient and 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).
GTDB-Tk is under active development and validation. Please independently confirm the GTDB-Tk predictions by manually inspecting the tree and bringing any discrepencies to our attention. Notifications about GTDB-Tk releases will be available through the ACE Twitter account (https://twitter.com/ace_uq).
- ~90Gb of memory to run.
- ~70Gb of Storage.
- Python libraries
GTDB-Tk requires the following Python libraries:
- jinja2 >=2.7.3: a full featured template engine for Python.
- mpld3 >= 0.2: D3 viewer for Matplotlib.
- biolib >= 0.0.44: Python package for common tasks in bioinformatic.
- dendropy >= 4.1.0: A Python library for phylogenetics and phylogenetic computing: reading, writing, simulation, processing and manipulation of phylogenetic trees (phylogenies) and characters.
- SciPy Stack: at least the Matplotlib, NumPy, and SciPy libraries
Jinja2, mpld3, dendropy and biolib will be installed as part of GTDB-Tk when installing it via pip ( method described below). The SciPy Stack must be installed separately.
- Third party software
GTDB-Tk makes use of the following 3rd party dependencies and assumes these are on your system path:
- Prodigal >= 2.6.2: Hyatt D, et al. 2012. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics, 28, 2223-2230.
- HMMER >= 3.1: Eddy SR. 2011. Accelerated profile HMM searches. PLoS Comp. Biol., 7, e1002195.
- pplacer >= 1.1: Matsen F, et al. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics, 11, 538.
- FastANI >= 1.0: Jain C, et al. 2017. High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries.bioRxiv. 256800.
- FastTree >= 2.1.9: Price MN, et al. 2010 FastTree 2 -- Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE, 5, e9490.
- Perl modules
GTDB-Tk also assumes the Python 2.7.x and Perl interpreters are on your system path.
note: Perl interpreter requires Moose, Bundle::BioPerl and IPC::Run modules. you can install those modules using CPAN:
perl -MCPAN -e"install Moose"
perl -MCPAN -e"install IPC::Run"
perl -MCPAN -e"install Bundle::BioPerl"
if perl -MCPAN -e"install Bundle::BioPerl" does not run on your server, please install BioPerl following the steps under this link.
Make sure that the installed Perl modules (.pm) paths are part of the @inc variable.
If not , The PERL5LIB ( or PERLIB) environment variable need to be updated the same way the PATH environment variable is updated. Every directory listed in this variable will be added to @inc.
i.e:
export PERL5LIB="$PERL5LIB:/path/to/moose/module:/path/to/ipc/module:/path/to/bioperl/module"
- GTDB-Tk reference data
for version < 0.0.8 GTDB-Tk requires ~70G+ of external data that need to be downloaded and unarchived (preferably in the same directory):
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/fastani.tar.gz
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/markers.tar.gz
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/masks.tar.gz
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/msa.tar.gz
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/pplacer.tar.gz
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/taxonomy.tar.gz
Or alternatively, all the data at once using:
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/gtdbtk_rxx_data.tar.gz
for version 0.0.8+ GTDB-Tk requires ~25G+ of external data that need to be downloaded and unarchived :
wget https://data.ace.uq.edu.au/public/gtdbtk/release_xx/gtdbtk_rxx_archived_data.tar.gz
tar xvzf gtdbtk_rxx_archived_data.tar.gz
- pip installation
Once these are installed, GTDB-Tk can be installed using pip:
> pip install gtdbtk
GTDB-Tk requires a config file. In the Python lib/site-packages directory, go to the gtdbtk directory and setup this config file:
cd config
cp config_template.py config.py
Edit the config.py file and modify different variables:
-GENERIC_PATH should point to the directory containing the data downloaded from the https://data.ace.uq.edu.au/public/gtdbtk/.
note: Make sure the variable finishes with a slash '/'.
The functionality provided by GTDB-Tk can be accessed through the help menu:
> gtdbtk -h
Usage information about each methods can also be accessed through their species help menu, e.g.:
> gtdbtk classify_wf -h
The classify workflow consists of three steps: identify, align, and classify. The identify step calls genes using Prodigal and then uses HMM models and the HMMER package to identify the marker genes used for phylogenetic inference. Consistent alignments are obtained by aligning marker genes to their respective HMM model. The align step concatenates the aligned marker genes and applies all necessary filtering to the concatenated multiple sequence alignment (MSA). Finally, the classify step uses pplacer to find the maximum-likelihood placement of each genome's concatenated protein alignment in the GTDB-Tk reference tree. GTDB-Tk classifies each genome based on its placement in the reference tree, its relative evolutionary distance, and FastANI distance (see Chaumeil PA et al., 2018 for details).
The classify workflow can be run as follows:
> gtdbtk classify_wf --genome_dir <my_genomes> --out_dir <output_dir>
This will process all genomes in <my_genomes> using both bacterial and archaeal marker sets and place the results in <output_dir>. Genomes must be in FASTA format. The location of genomes can also be specified using a batch file with the --batchfile flag. The batch file is simply a two column file indicating the location of each genome and the desired genome identifier (i.e., a Newick compatible alphanumeric string). These fields must be seperated by a tab.
The workflow supports several optional flags, including:
- cpus: maximum number of CPUs to use
For other flags please consult the command line interface.
Here is an example run of this workflow:
> gtdbtk classify_wf --cpus 24 --genome_dir ./my_genomes --out_dir gtdbtk_output
The taxonomic classification of each bacterial and archaeal genome is contained in the <prefix>.bac120.classification.tsv and <prefix>.ar122.classification.tsv output files.
Each step of the classify workflow generates a number of files that can be consulted for additional information about the processed genomes.
Identify step:
- <prefix>_bac120_markers_summary.tsv: summary of unique, duplicated, and missing markers within the 120 bacterial marker set for each submitted genome
- <prefix>_ar122_markers_summary.tsv: analogous to the above file, but for the 122 archaeal marker set
- marker_genes directory: contains individual genome results for gene calling using Prodigal and gene identification based on TIGRFAM and Pfam HMMs
Align step:
- <prefix>.user_msa.fasta: FASTA file containing MSA of the submitted genomes
- <prefix>.msa.fasta: FASTA file containing MSA of submitted and reference genomes
- <prefix>.filtered.tsv: list of genomes with an insufficient number of amino acids in MSA
Classify step:
- <prefix>.summary.tsv: classification of user genomes based on the FastANI, RED values, and pplacer. This is the primary output of the GTDB-Tk and contains the taxonomic classification we recommend plus additional information regarding the criteria used to classify a genome
- <prefix>.classification_pplacer.tsv: classification of user genomes based only on pplacer
- <prefix>.classify.tree: reference tree in Newick format containing all user genomes placed with pplacer in the GTDB-Tk reference tree
- <prefix>.red_dictionary: median RED values for taxonomic ranks
The GTDB-Tk uses FastANI to estimate the average nucleotide identity (ANI) between genomes. Species assignments are made using an ANI criteria of 95%. Information about species assignments can be found in the <prefix>.fastani_results.tsv output file.
under active development The de novo workflow infers a new tree containing all user supply and GTDB-Tk reference genomes. The classify workflow is recommended for obtaining taxonomic classifications, and this workflow only recommended if a de novo tree is desired. This workflow consists of five steps: identify, align, infer, root, and decorate. The identify and align steps are the same as in the classify workflow. The infer step uses FastTree with the WAG+GAMMA models to calculate a de novo tree. This tree is then rooted using a user specified outgroup and decorated with the GTDB taxonomy.
The de novo workflow can be run as follows:
> gtdbtk de_novo_wf --genome_dir <my_genomes> --<marker_set> --outgroup_taxon <outgroup> --out_dir <output_dir>
This will process all genomes in <my_genomes> using the specified marker set and place the results in <output_dir>. Only genomes previously identified as being bacterial (archaeal) should be included when using the bacterial (archaeal) marker set. The tree will be rooted with the taxon. Identical to the classify workflow, the location of genomes can also be specified using a batch file with the --batchfile flag.
The workflow supports several optional flags, including:
- cpus: maximum number of CPUs to use
- min_perc_aa: filter genomes with an insufficient percentage of AA in the MSA (default: 50)
- taxa_filter: filter genomes to taxa within specific taxonomic groups
- prot_model: protein substitution model for tree inference (LG or WAG; default: WAG)
For other flags please consult the command line interface.
Here is an example run of this workflow:
> gtdbtk de_novo_wf --genome_dir ./genomes --bac120_ms --outgroup_taxon p__Acetothermia --taxa_filter p__Firmicutes --out_dir de_novo_output
All steps comprising the classify and de novo workflows can be run independently if desired. Please consult the command line interface for specific details on running each of these steps.
A manuscript describing the GTDB-Tk is currently being prepared:
- Chaumeil PA, Hugenholtz P, Parks DH. 2018. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. <in prep>.
In the meantime, if you find the GTDB-Tk useful please cite this GitHub page.
The GTDB taxonomy is described in:
- Parks DH, et al. 2018. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol., http://dx.doi.org/10.1038/nbt.4229
We also strongly encourage you to cite the following 3rd party dependencies:
- Matsen FA, Kodner RB, Armbrust EV. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics, 11:538.
- Jain C, et al. 2017. High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries. bioRxiv, https://doi.org/10.1101/225342.
- 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, Dehal PS, Arkin AP. 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 © 2017 Pierre-Alain Chaumeil. See LICENSE for further details.