GPMsDB-tk v1.0.1 was released on March 7, 2023.
GPMsDB-tk/GPMsDB-dbtk are software toolkits for assigning taxonomic identification to user-provided MALDI-TOF mass spectrometry profiles obtained from bacterial and archaeal cultured isolates. They take advantages of a newly developed database of protein mass profiles predicted from ~200,000 bacterial and archaeal genome sequences. This toolkit is also designed to work with customized databases, allowing microbial identification based on user-provided genome/metagenome-assembled genome (MAG) sequences. The GPMsDB-tk is open source and released under the GNU General Public License (Version 3).
Please post questions and issues related to GPMsDB-tk on the Issues section of the GitHub repository.
Prerequisites
- Python (version 3.7 or higher)
- Cython (version 0.29.1 or higher)
- scipy (version 1.7.3 or higher)
- matplotlib (version 3.5.0 or higher)
In the source directory, the following command will compile and install the software in your python environment.
git clone https://github.com/ysekig/GPMsDB-tk
cd GPMsDB-tk
python setup.py installDuring the installation, you may see some deprecation warnings like “easy_install command is deprecated” but this will not cause any issues for GPMsDB-tk.
GPMsDB-tk requires ~7 GB of external data that needs to be downloaded from Zenodo (DOI: 10.5281/zenodo.8245428) and unarchived:
tar xvzf R01-RS95.tar.gzGPMsDB-tk requires an environment variable named GPMsDB_PATH to be set to the directory containing the unarchived reference data.
export GPMsDB_PATH=/path/to/release/package/If you are interested in cu 71B9 stomizing the database with user-provided genomes/metagenome-assembled genomes (MAGs), GPMsDB-dbtk should also be installed.
- Peak-list characterization:
- inspect -> Inspection of a peak-list and generate peak plots
- adjust -> m/z adjustment for given peak-list
- Strain identification based on peak-list(s)
- identify -> Search for the best-matching genome(s) without m/z adjustment
- identify_wf -> Full identification workflow (option "-aa" should be set for m/z adjustment)
- identify_bwf -> Full identification workflow for a batch of files (option "-aa" should be set for m/z adjustment)
- Peak annotation
- peak -> Annotate protein names and Tigrfam/Pfam markers genes
- peak_wf -> Full peak-list characterization workflow (option "-aa" should be set for m/z adjustment)
- peak_bwf -> Full peak-list characterization workflow for a batch of files (option "-aa" should be set for m/z adjustment)
- protein_hit: number of hits with all proteins predicted for reference genome
- ribosomal_hit: number of hits with ribosomal proteins predicted for reference genome
- score: matching value calculated for reference genome (higher better matching)
- probability value: the frequency of appearance of a given score inferred based on scores from 100 randomely selected reference genomes
- likelihood(%): likelihood of correct identification (empirical, based on ribosomal_hit and probability.
- ncbi_name: NCBI oraganism name (genus/species) for reference genome
- ncbi_strain: NCBI strain name for reference genome
- taxonomy_gtdb: GTDB taxonomy string for reference genome
Please report bugs through the GitHub issues system, or contact Yuji Sekiguchi (y.sekiguchi@aist.go.jp)
Copyright (C) 2023 Yuji Sekiguchi, National Institute of Advanced Industrial Science and Technology (AIST)
This package is under the conditions of the GNU General Public License (Version 3). See LICENSE for further details.