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Python query interface of Datanator, a toolkit for discovering the data needed for modeling the biochemistry of cells.



A central goal of synthetic biology is to rationally design organisms. Similarly, a central goal of precision medicine is to tailor therapy to each patient based on their unique genome. Many engineering fields use computer-aided design (CAD) tools driven by mechanistic models to efficiently design complex systems such as planes. Analogously, more comprehensive and more predictive models of biological systems, such as whole-cell models, are needed to help bioengineers and physicians design biomachines and medical therapy.

One of the biggest bottlenecks to achieving such models is collecting and aggregating the large amount of data needed for model construction and verification. Due to advances in genomics and increased emphasis on data sharing, there is now extensive data about a wide range of biochemical entities and processes such as data about metabolite concentrations, RNA and protein abundances, and reaction rates. However, it remains difficult to utilize this information for modeling because the data scattered across numerous databases and publications; the data is described using different formats, identifiers, and units; and there are inadequate tools for finding relevant data for modeling a specific biological system in a specific environment.

The Datanator toolkit seeks to address these problems for biochemical modeling by providing investigators an integrated database of molecular data and tools for discovering relevant data for modeling projects and other meta analyses. Please see the About page for more information about the goals and features of Datanator.

The Datanator toolkit is composed of the following packages:

  • Datanator: Tools for aggregating and integrating diverse data from diverse sources into a single dataset and searching these datasets
  • Datanator-db: MongoDB server for Datanator-data
  • Datanator-fulltext-db: ElasticSearch server for Datanator-data
  • Datanator-query-python: This package, tools for querying Datanator-db and Datanator-fulltext-db
  • Datanator-rest-api: REST interface for Datanator-query-python
  • Datanator-frontend: A web-based graphical user interface to Datanator-db.

This package provides a pythonic interface to programmatically access data stored in Datanator-db and Datanator-fulltext-db.

Testing, installation, and usage


We recommend using the REST API to programmatically access data stored in Datanator-db and Datanator-fulltext-db.


Install Datanator-query-python

  1. Install Python >= 3.7
apt-get install python3
  1. Install git
apt-get install git
  1. Clone this repository
git clone
  1. Install this package
cd /path/to/datanator_query_python
pip install -r requirements.txt
pip install -e .
  1. Setup environment variables (credentials for accessing databases)

Test Datanator-query-python

  1. Run the unit tests
python -m pytest test-unit-coverage

File organization

This repository is organized as follows:

  • datanator_query_python/:
    • aggregate/: aggregation pipelines for MongoDB
    • config/: datanator_query_python configuration
    • query/: collection-based query scripts
    • query_schema_2/: data-type-based query scripts
    • util/: utility tools
  • tests/
    • aggregate/ - test scripts in aggregate directory
    • config/ - test scripts in config directory
    • query/ - test scripts in query directory
    • util/ - test scripts in util directory
  • CODE_OF_CONDUCT: code of conduct
  • LICENSE: license
  • requirements.txt: package configuration
  • README: overview of the application

Development and deployment workflow

  1. Create a Git new branch
  2. Commit code to the new branch
  3. Push the branch to GitHub
  4. GitHub will automatically trigger CircleCI to run the unit test, integration tests, and other static analyses
  5. Use Coveralls to review the coverage of the tests
  6. As needed, add additional tests and fix any failing tests
  7. Once the new code passes the tests and has high coverage, create a pull request to merge the new branch into the master branch
  8. One of the main developers will review the pull request and request changes as necessary
  9. Once any necessary changes have been made, one of the main developers will approve the pull request
  10. GitHub will automatically trigger CircleCI to run the unit test, integration tests, and other static analyses for the master branch
  11. Once the CircleCI build succeeds, downstream dependencies will automatically be tested and built

Contributing to Datanator

We welcome contributions to Datanator via GitHub pull requests. Please contact the developers to coordinate potential contributions, and please see above for information about how to submit pull requests.


This package is released under the MIT license.

Development team

This package was developed by the Karr Lab at the Icahn School of Medicine at Mount Sinai in New York by the following individuals:

Questions and comments

Please submit an issue, or contact the Karr Lab with any questions or comments.


Datanator was developed with support from the Center for Reproducible Biomedical Modeling from the National Institute of Bioimaging and Bioengineering and the National Institute of General Medical Sciences of the National Institutes of Health and the National Science Foundation (awards P41EB023912 and R35GM119771).


A package to query and format the data in the integrated Datanator database







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