Preprocess BioCyc files

Basic Description

prepBioCyc is a collection of modules for

• preprocessing BioCyc database collection, including MetaCyc (step 1)
• building association matrices among pathways, ECs, and compounds (step 2)
• extracting EC and pathway properties while building mapping files for the downstream PathoLogic and MinPath prediction algorithms (steps 3-4)
• construct pairwise similarities among pathways (step 5)
• building gene, EC, and pathway graphs (step 6)
• creating synthetic and golden datasets with features (step 7-8)

Dependencies

The codebase is tested to work under Python 3.8. To install the necessary requirements, run the following commands:

pip install -r requirements.txt

Basically, prepBioCyc requires following packages:

• Anaconda
• NumPy (>= 1.19)
• scikit-learn (>= 0.24)
• NetworkX (>= 2.4)
• scipy (>=1.6)
• fuzzywuzzy (== 0.18.0)

Installation and Basic Usage

Run the following commands to clone the repository to an appropriate location:

git clone https://github.com/hallamlab/prepBioCyc.git

For all experiments, navigate to src folder then run the commands of your choice. For example, to display * prepBioCyc*"s running options use: python main.py --help. It should be self-contained. For a general usage execute the following command:

python main.py --build-biocyc-object --build-indicator --build-pathway-properties --build-ec-properties --build-pathway-similarities --build-graph --constraint-kb 'metacyc' --build-synset --ex-features-from-synset --build-golden-dataset --ex-features-from-golden-dataset --build-pathologic-input --build-minpath-dataset --minpath-map --kbpath "[path to database]" --ospath "[path to the object files (e.g. 'biocyc.pkl')]" --dspath "[path to dataset and to store results]" --display-interval -1 --num-jobs 2

Please obtain MetaCyc and other databases from BioCyc.

Citing

If you find prepBioCyc useful in your research, please consider citing this repo and the following papers:

• M. A. Basher, Abdur Rahman, McLaughlin, Ryan J., and Hallam, Steven J.. "Metabolic pathway inference using multi-label classification with rich pathway features." , PLoS Comput Biol (2020).
• M. A. Basher, Abdur Rahman, and Hallam, Steven J.. "Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction" , Bioinformatics (2020).
• M. A. Basher, Abdur Rahman, McLaughlin, Ryan J., and Hallam, Steven J.. "Metabolic pathway inference using non-negative matrix factorization with community detection." , 10th International Conference on Computational Advances in Bio and medical Sciences (ICCABS), (2020).
• M. A. Basher, Abdur Rahman and Hallam, Steven J.. "Multi-label pathway prediction based on active dataset subsampling." , bioRxiv (2020).
• M. A. Basher, Abdur Rahman and Hallam, Steven J.. "reMap: Relabeling Multi-label Pathway Data with Bags to Enhance Predictive Performance" , bioRxiv (2020).
• M. A. Basher, Abdur Rahman. "Modeling Metabolic Pathways as Groups (with Augmentation).", arXiv (2019).

Contact

For any inquiries, please contact: arbasher@student.ubc.ca