datanator

datanator is a software tool for finding experimental data for building and calibrating dynamical models of cellular biochemistry such as metabolite, RNA, and protein abundances; protein complex compositions; transcription factor binding motifs; and kinetic parameters. datanator is particularly useful for building large models, such as whole-cell models, that require large amounts of data to constrain large numbers of parameters. datanator was motivated by the need for large amounts of data to constrain whole-cell models and the fact that this data is hard to utilize because it is scattered across numerous siloed repositories.

datanator currently supports the following data types and data sources:

• Metabolite concentrations: ECMDB and YMBD
• RNA abundance: ArrayExpress
• Protein abundance: PaxDb
• Protein complex composition: CORUM
• Transcription factor binding motifs: JASPAR
• Reaction kinetics: SABIO-RK
• Taxonomy: NCBI Taxonomy

datanator (1) downloads these repositories; (2) normalizes their data to a common ontology and units; (3) stores their data to a local SQLite database; and (4) provides a Python API for (a) finding relevant data to model a specific organism and environmental condition from similar species, reactions, genotypes (taxon, variant), and environments (temperature, pH, media), and (b) reducing multiple relevant observations to a single consensus recommended parameter value, and (c) exporting these consensus recommendations and their provenance to an Excel workbook. To make datanator easier to use, we plan to develop user-friendly command line and web-based interfaces for finding data for SBML-encoded models.

datanator is under active development and is not yet ready for end users. Please check back soon for updates.

This website contains detailed documentation of the datanator source code. Going forward, this website will also contain detailed instructions and tutorials on how to use datanator.

Contents

• 1. Overview
  • 1.1. Motivation
  • 1.2. Methodology
  • 1.3. Supported data types and data sources
  • 1.4. Advantages of datanator versus manual data aggregation
• 2. Installation
  • 2.1. Install dependencies
  • 2.2. Install datanator
  • 2.3. Run datanator
• 3. Tutorial - Getting Familiar With datanator’s Command Line Interface
  • 3.1. Generate Template Doc
  • 3.2. Collecting Kinetic Data
  • 3.3. Entering Reactions Into datanator
  • 3.4. Set Maximum Proximity Limit
• 4. API documentation
  • 4.1. datanator package
    • 4.1.1. Subpackages
    • 4.1.2. Submodules
    • 4.1.3. datanator.__main__ module
    • 4.1.4. datanator.datanator module
    • 4.1.5. datanator.io module
    • 4.1.6. Module contents
• 5. About
  • 5.1. License
  • 5.2. Development team
  • 5.3. Acknowledgements
  • 5.4. Questions and comments
• 6. References