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<p align="center">
<img src="https://github.com/draeger-lab/refinegems/raw/main/docs/source/images/refineGEMs_logo.png" height="200"/>
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# refineGEMs
`refineGEMs` is a python package intended to help with the curation of genome-scale metabolic models (GEMS). </br>
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),
- Updating the annotation of metabolites and extending the model with reactions (for the purpose of filling gaps) based on a table filled by the user `data/manual_annotations.xlsx` (Note: This only works when the structure of the [example Excel file](https://github.com/draeger-lab/refinegems/blob/5eac900d9848b5ae5faf0055db72a986e7ba64e8/data/manual_curation.xlsx) is used.),
- And extending the model with all information surrounding reactions including the corresponding GeneProducts and metabolites by filling in the table `data/modelName_gapfill_analysis_date_example.xlsx` (Note: This also only works when the structure of the [example Excel file](https://github.com/draeger-lab/refinegems/blob/5eac900d9848b5ae5faf0055db72a986e7ba64e8/data/modelName_gapfill_analysis_date_example.xlsx) is used).
## 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 `setup.py` 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=3.9
conda activate <EnvName>
# check that pip comes from <EnvName>
which pip
pip install .
```
## 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`
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