Metadata-Version: 2.4
Name: gemsembler
Version: 0.11.7
Summary: A tool for assembling and comparing several types of Genome-Scale Metabolic Models.
Author-email: Elena Matveishina <elena.matveishina@embl.de>, Bartosz Bartmanski <bartosz.bartmanski@embl.de>
License: MIT License
        
        Copyright (c) 2024 Zimmermann-Kogadeeva Group
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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Project-URL: Homepage, https://git.embl.de/grp-zimmermann-kogadeeva/GEMsembler
Project-URL: Bug tracker, https://git.embl.de/grp-zimmermann-kogadeeva/GEMsembler/issues
Keywords: genome scale metabolic models,metabolism,biology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cobra
Requires-Dist: metquest
Requires-Dist: dill
Requires-Dist: ncbi-genome-download
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: networkx
Requires-Dist: pandas>=2.0
Requires-Dist: pyarrow
Requires-Dist: seaborn
Requires-Dist: pyvis
Requires-Dist: h5py
Requires-Dist: platformdirs
Requires-Dist: pyyaml
Requires-Dist: scipy<=1.14
Dynamic: license-file

# GEMsembler

[![Documentation](https://img.shields.io/badge/docs-passing-green)](https://grp-zimmermann-kogadeeva.embl-community.io/GEMsembler)

<img src="gemsembler_long.gif" alt="drawing" width="150"/>

GEMsembler tool for assembling and comparing several types of Genome-Scale Metabolic
Models. 

**THIS IS A BETA VERSION! BUGS CAN BE EXPECTED**

**Version 0.9.0 is incompatible with previous versions**

## Installation

Install with the following command:
```
pip install gemsembler
```

**Note:** you also have to install BLAST in advance.

## Usage

Input models have to be COBRApy readable files. And models need to be
particular type. Currently models made by CarveMe (carveme), ModelSEED
(modelseed), gapseq (gapseq) and models downloaded from AGORA VMH database
(agora) are supported. Custom type is coming soon. Genomes, from which the
models are built will allow to convert and assemble genes as well.
First, we import gemsembler and get the path to data files:
```
from gemsembler import GatheredModels, lp_example, get_model_of_interest
```
lp_example is a list with input models and related inforamtion such as model type, corresponding genome and so on.
```
lp_example = [
    dict(
        model_id="curated_LP",
        path_to_model=files(LP) / "LP_iLP728_revision_data_met_C_c.xml.gz",
        model_type="carveme",
        path_to_genome=files(LP) / "LP_protein_fasta.faa.gz",
    ),
    dict(
        model_id="cauniv_LP",
        path_to_model=files(LP) / "LP_CA1.xml.gz",
        model_type="carveme",
        path_to_genome=files(LP) / "LP_protein_fasta.faa.gz",
    ),
    dict(
        model_id="cagram_LP",
        path_to_model=files(LP) / "LP_CA2.xml.gz",
        model_type="carveme",
        path_to_genome=files(LP) / "LP_protein_fasta.faa.gz",
    ),
    dict(
        model_id="msgram_LP",
        path_to_model=files(LP) / "LP_MS2.sbml.gz",
        model_type="modelseed",
        path_to_genome=files(LP) / "LP_protein_fasta.faa.gz",
    ),
    dict(
        model_id="agora_LP",
        path_to_model=files(LP) / "LP_WCFS1_agora.xml.gz",
        model_type="agora",
        path_to_genome=files(LP) / "LP_WCFS1.fasta.gz",
    ),
]
```

First stage is the creation of gathered models, a class, that performs
conversion and contains results of all stages:
```
gathered = GatheredModels()
for model in lp_example:
    gathered.add_model(**model)
gathered.run()
```
Second stage is actual assembly of supermodel from the in formation in gathered
models. User has to provide output folder. And for gene conversion user hast
provide either final genes in fasta. Then all gene will be converted to ids in
these files. Or if user provides NCBI assembly ID for his organism of interest,
corresponding genome will be downloaded automatically and all genes will be
converted to the locus tags of the organism.
```
supermodel_lp = gathered.assemble_supermodel("./gemsembler_output/", assembly_id = "GCF_000203855.3")
```
After supermodel is assembled different comparison methods can be run
```
supermodel_lp.at_least_in(2)
```
And results of comparison can be extracted as typical COBRApy models
```
core2 = get_model_of_interest(supermodel_lp, "core2", "./gemsembler_output/LP_core2_output_model.xml")
```
