These scripts create an up-to-date version of the Unknome database, as a relational database in SQLite3 file format. A helper script is included to create summary files and charts from a database file, as used on the Unknome web site. These scripts compile the data that is made available on the Unknome web site, but remain separate from the site itself.
The overall principle of the Unknome database is to assign a knownness score to proteins, representing the amount of human knowledge they have. Proteins from the UniProt database are placed in a cluster of orthologues based on the Panther database. The knowness score is defined as the largest number of Gene Ontology terms that has been assigned to a member of that cluster. Because GO annotations vary in confidence and relevance to function, different types of evidence aree assigned a different weight when calculating the score.
If you are looking for the scripts that were used in the analysis of Drosophila phenotype screens done for the Unknome paper please see the other unknome repository.
These scripts work with vanilla Python (v3) and have no requirements outside of the standard library. They have no special in stallation requirements and can be run directly from a cloned repository.
If you move the setup_unknome_db.py Python script the constants.py and sql_tables.py modules must be
on the import PYTHONPATH.
The setup_unknome_db.py is simply run from the command line using a Python3 interpreter, including the working directory (for storing GO and PANTHER data files etc) and any other command line options (see below).
Typical use:
python3 setup_unknome_db.py /save_dir_path/
The input data, from which the Unknome database is created, will be downloaded from various Internet resources.
These data, with their default locations, are as follows:
As time passes the default values for these URLs may no longer work. However, all the URLs may be specified as command line options in the event that they change.
The compiled database is generated as a single SQLite3 format file, which may be interrogated with the standard sqlite3 Python module. See the sql_tables.py file from an indication fo the SQL schema.
usage: setup_unknome_db [-h] [-o OUT_DB_FILE] [-f] [-eco FILE_URL]
[-ps WEB_URL] [-pd DIR_URL] [-gaf FILE_URL]
[-tax FILE_URL]
WORKING_DIR
positional arguments:
WORKING_DIR The working directory, into which downloaded data files and
the databse .sqlite file will be stored.
optional arguments:
-h, --help show this help message and exit
-o OUT_DB_FILE Optional output file path to write the SQLite3 database file
to. The default is to create a file in the working
directorly labelled by creation date, of the form
"unknome_db_{date}.sqlite".
-f, --force Whether to force aa re-download of all data files, if
already present.
-eco FILE_URL URL to download evidence code definitions, as used by GO, as
an "eco.obo" file. Default=https://raw.githubusercontent.com
/evidenceontology/evidenceontology/master/eco.obo
-ps WEB_URL URL for PANTHER proteome summary table, to obtain a list of
species.
Default=https://www.pantherdb.org/panther/summaryStats.jsp
-pd DIR_URL URL for the DIRECTORY containing the latest PANTHER HMM
classifications file. Default=http://data.pantherdb.org/ftp/
hmm_classifications/current_release/
-gaf FILE_URL URL for GeneOntology to UniProt linking GOA .gaf file. Defau
lt=https://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_un
iprot_all.gaf
-tax FILE_URL URL for TAR GZIPPEd archive containing NCBI taxonomy .dmp
files. Default=https://ftp.ncbi.nih.gov/pub/taxonomy/new_tax
dump/new_taxdump.tar.Z
If you use these scripts in your research please cite:
"Functional unknomics: Systematic screening of conserved genes of unknown function" Joao Rocha, Satish Arcot Jayaram, Tim J Stevens, Nadine Muschalik, Rajen D Shah, Sahar Emran, Cristina Robles, Matthew Freeman, Sean Munro PLoS Biol. 2023 Aug; 21(8): e3002222 PMID: 37552676
We have chosen to apply the Creative Commons Attribution 4.0 International (CC BY 4.0) License to any and all copyrightable parts of the Unknome database. We make no warranties regarding the correctness of the data presented, and disclaim liability for any damages that may result from its use. Users of the data are solely responsible for compliance with any copyright restrictions, patents or other rights. All data is provided “as-is” without any warranty, expressed or implied.