Metadata-Version: 2.1
Name: SBMate
Version: 1.1.2
Summary: Annotation quality metrics calculator (coverage, consistency, specificity).
Home-page: https://github.com/woosubs/SBMate
Author: Woosub Shin
Author-email: woosubs@umich.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
License-File: LICENSE

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# SBMate
Systems Biology Model AnnoTation Evaluator

## Overview
``SBMate`` evaluates the quality of annotations in SBML model elements, especially libsbml.Model, libsbml.Species, libsbml.Compartment, and libsbml.Reaction. Currently, it examines annotations from five knowledge resources, CHEBI, GO, KEGG, SBO, and UNIPROT. 
 
SBMate calculates three metrics: 
1. Coverage checks how many model elements of the above four types (model, reaction, species, and compartment) are actually annotated. 
2. Consistency computes how many of such annotated entities has proper annotation. For example, a reaction object should not have a GO cellular component term (GO:0005575 or its children). SBMate identifies such instances and calculates the proprotion of model entities whose annotations are consistent. 
3. Finally, specificity is a measure of how 'precise' such consistent annotations are. This is obtained by utilizing the hierarchical structures of knowledge resource terms, such as the directed acyclic graphs of SBO, GO and CHEBI. 

More detailed discussions can be found in our manuscript (in preparation). 

## Example
It is quite easy to use SBMate as there is just one main method, ``sbmate.AnnotationMetrics.getMetrics``.

<img src="https://github.com/woosubs/SBMate/raw/main/png/run_sbmate.png" width="800"/>

By default, SBMate produces a report summarizing the three scores:

<img src="https://github.com/woosubs/SBMate/raw/main/png/result_report.png" width="800"/>

Another option is to create a pandas DataFrame, as below:

<img src="https://github.com/woosubs/SBMate/raw/main/png/run_sbmate_table.png" width="800"/>

And you will get the dataframe. 

<img src="https://github.com/woosubs/SBMate/raw/main/png/result_table.png" width="800"/>

## Adding Additional Metrics
You can add additional metrics by creating a class that calculates metrics.
Metric values are contained in a ``pandas`` ``DataFrame``.
See ``metric_calculator.py`` to see how to write a class that calculates metrics.
When you construct ``AnnotationMetrics``, you will assign a value to the keyword argument ``metric_calculator_classes``
of the constructor.


