refineGEMs 2.0.0b1
refineGEMs: a python package intended to help with the curation of genome-scale metabolic models (GEMS)
License: MIT
Requires Python: <3.13,>=3.10
Keywords: Systems Biology, GEM, Metabolic Modelling, Python Package

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# refineGEMs
`refineGEMs` is a python package intended to help with the curation of genome-scale metabolic models (GEMS). </br>
⚠️🚧 **DISCLAIMER:** The documentation is currently under heavy-rework!
The documentation can be found [here](https://refinegems.readthedocs.io/en/latest/).🚧

## Table of contents
1. [Overview](#overview)
2. [Installation](#installation)
3. [How to cite](#how-to-cite)
4. [Repositories using refineGEMs](#repositories-using-refinegems)

## Overview

Currently `refineGEMs` can be used for the investigation of a GEM, it can complete the following tasks:

- loading GEMs with `COBRApy` and `libSBML`
- report number of metabolites, reactions and genes
- report orphaned, deadends and disconnected metabolites
- report mass and charge unbalanced reactions
- report [Memote](https://memote.readthedocs.io/en/latest/index.html) score
- compare the genes present in the model to the genes found in:
  - the [KEGG](https://www.genome.jp/kegg/kegg1.html) Database (Note: This requires the GFF file and the KEGG identifier of your organism.)
  - Or the [BioCyc](https://biocyc.org) Database (Note: This requires that a database entry for your organism exists in BioCyc.)
- compare the charges and masses of the metabolites present in the model to the charges and masses denoted in the [ModelSEED](https://modelseed.org/) Database.

Other applications of `refineGEMs` to curate a given model include: 

- The correction of a model created with [CarveMe](https://github.com/cdanielmachado/carveme) v1.5.1 or v1.5.2 (for example moving all relevant information from the notes to the annotation field or automatically annotating the GeneProduct section of the model with the respective NCBI gene/protein identifiers from the GeneProduct identifiers),
- The addition of [KEGG](https://www.genome.jp/kegg/kegg1.html) Pathways as Groups (using the [libSBML](https://synonym.caltech.edu/software/libsbml/5.18.0/docs/formatted/python-api/classlibsbml_1_1_groups_model_plugin.html) Groups Plugin),
- Updating the SBO-Term annotations based on [SBOannotator](https://github.com/draeger-lab/SBOannotator),


## Installation

You can install `refineGEMs` via pip:

```bash
pip install refineGEMs

```

or to a local conda environment where `refineGEMs` is distributed via this GitHub repository and all dependencies are denoted in the `pyproject.toml` file:

```bash
# clone or pull the latest source code
git clone https://github.com/draeger-lab/refinegems.git
cd refinegems

conda create -n <EnvName> python=3.10 (at least but < 3.13)

conda activate <EnvName>

# check that pip comes from <EnvName>
which pip

pip install .

```

``refineGEMs`` depends on the tools [MCC](https://github.com/Biomathsys/MassChargeCuration) and 
[BOFdat](https://github.com/draeger-lab/BOFdat) which cannot directly be installed via PyPI or the `pyproject.toml`. 
Please install both tools before using ``refineGEMs``:

```bash
# For MCC, until hot fix is merged into main:
pip install "masschargecuration@git+https://github.com/Biomathsys/MassChargeCuration"

# For BOFdat, our fork with hot fix(es):
pip install "bofdat@git+https://github.com/draeger-lab/BOFdat"

```

## How to cite
When using `refineGEMs`, please cite the latest publication:

Famke Bäuerle, Gwendolyn O. Döbel, Laura Camus, Simon Heilbronner, and Andreas Dräger. 
Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium
striatum. Front. Bioinform., oct 2023. [doi:10.3389/fbinf.2023.1214074](https://doi.org/10.3389/fbinf.2023.1214074).

## Repositories using refineGEMs
- [C_striatum_GEMs](https://github.com/draeger-lab/C_striatum_GEMs)
- draeger-lab/Shaemolyticus - `private`
- draeger-lab/Ssanguinis - `private`