MINE Databases

The MINE Database contains code for generating (through Pickaxe) and storing and retrieving compounds from a database. Pickaxe applies reaction rules, representing reaction transformation patterns, to a list of user-specified compounds in order to predict reactions.

Documentation

For general information on MINE Databases, please consult JJeffryes et al. 2015..

Documentation is hosted at https://mine-database.readthedocs.io/en/latest/. It gives more detailed descriptions and example uses of the software.

Installation (legacy)

If a conda environment is desired to be used:

conda create -n mine

conda activate mine

Then, use pip (with or without conda) to install minedatabase:

pip install minedatabase

Installation (current)

You need Poetry (https://python-poetry.org/)

poetry install

Also be sure to install MongoDB (https://docs.mongodb.com/manual/installation/) if you are using the write_db = True option in Pickaxe. Installation information on Ubuntu system https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-ubuntu/

Running Pickaxe (legacy)

Running Pickaxe through a python file (recommended)

An example file, pickaxe_run_template.py, provides a framework for running pickaxe through a python file. Feel free to download it and change it to your needs. The starting compounds, rules and cofactors, optional database information, and Pickaxe run options are specified. After running the results are stored in a specified database or written to .tsv files.

This is all explained in more detail in the documentation.

Pickaxe command-line usage (not recommended - see above section)

Pickaxe.py can be called independently to generate predictions with or without database storage. To list all options call python -m minedatabase.pickaxe -h. Note that due to relative imports, it needs to be run as a module (-m flag) from the MINE-Database directory. To predict metacyc reactions for one generation on compounds in the iML1515 model one would call

python pickaxe.py -C ./data/metacyc_rules/metacyc_generalized_rules.tsv -r ./data/metacyc_rules/metacyc_coreactants.tsv -g 1 -c ../example_data/iML1515_ecoli_GEM.csv

Running Pickaxe (current)

Running Pickaxe through the CLI (recommended)

Activate the poetry shell:

poetry shell

Then, run the following command:

python pickaxe_run_template.py

For now you will need to edit all your parameters directly inside the corresponding script.

We now try to clean the previous messy command.

python pickaxe.py -C ./data/metacyc_rules/metacyc_generalized_rules.tsv -r ./data/metacyc_rules/metacyc_coreactants.tsv -g 1 -c ../example_data/iML1515_ecoli_GEM.csv

Should be

python mine_database/pickaxe.py -C ./mine_database/data/metacyc_rules/metacyc_coreactants.tsv -r ./mine_database/data/metacyc_rules/metacyc_generalized_rules.tsv  -g 1 -c ./example_data/iML1515_ecoli_GEM.csv -o ./data/

Quick example:

Using MetaCyc rules and coreactants.

python mine_database/pickaxe_commons.py -C ./mine_database/data/metacyc_rules/metacyc_coreactants.tsv -r ./mine_database/data/metacyc_rules/metacyc_generalized_rules.tsv  -g 1 -c ./example_data/lotus_10.csv -o ./data/

Using original rules and coreactants (Enzymatic)

python mine_database/pickaxe_commons.py -C ./mine_database/data/original_rules/EnzymaticCoreactants.tsv -r ./mine_database/data/original_rules/EnzymaticReactionRules.tsv  -g 1 -c ./example_data/lotus_10.csv -o ./data/

python mine_database/pickaxe_commons.py -C ./mine_database/data/original_rules/EnzymaticCoreactants.tsv -r ./mine_database/data/original_rules/EnzymaticReactionRules.tsv  -g 1 -c ./example_data/lotus_1000.csv -o ./data/ -m 60

Legacy example:

python minedatabase/pickaxe_commons.py -C ./minedatabase/data/EnzymaticCoreactants.tsv -r ./minedatabase/data/EnzymaticReactionRules.tsv  -g 1 -c ./minedatabase/data/DNP_01.tsv -o ./data/

Complete command (writing to database):

python mine_database/pickaxe_commons.py --coreactant_list ./mine_database/data/original_rules/EnzymaticCoreactants.tsv --rule_list ./mine_database/data/original_rules/EnzymaticReactionRules.tsv --generations 1 --compound_file ./example_data/230106_frozen_metadata_for_mines.csv --output_dir ./data/ --processes 60 --verbose --explicit_h --database lotus_mines

Command without writing to database:

python mine_database/pickaxe_commons.py --coreactant_list ./mine_database/data/original_rules/EnzymaticCoreactants.tsv --rule_list ./mine_database/data/original_rules/EnzymaticReactionRules.tsv --generations 1 --compound_file ./example_data/lotus_10.csv --output_dir ./data/ --processes 60 --verbose --explicit_h

Command with generalized rules and full LOTUS dataset without writing to database:

python mine_database/pickaxe_commons.py --coreactant_list ./mine_database/data/metacyc_rules/metacyc_coreactants.tsv --rule_list ./mine_database/data/metacyc_rules/metacyc_generalized_rules.tsv --generations 1 --compound_file ./data/LOTUS_inputfiles/230106_frozen_metadata_inchy_smiles.csv --output_dir ./data/ --processes 60 --verbose --explicit_h

Accessing the mongodb database

To access the mongodb database, you can use the following command:

mongosh

Then, you can use the following commands to access the database:

use example_db
db.compounds.find()

Optionally the DB can be observed using MongoDB Compass. See https://www.mongodb.com/docs/compass/current/install/