| Name | gemsembler JSON |
| Version |
0.7.0
JSON |
| download |
| home_page | None |
| Summary | A tool for assembling and comparing several types of Genome-Scale Metabolic Models. |
| upload_time | 2024-05-17 09:12:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| 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 copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
genome scale metabolic models
metabolism
biology
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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**
## 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")
```
Raw data
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"description": "# GEMsembler\n\n<img src=\"gemsembler_long.gif\" alt=\"drawing\" width=\"150\"/>\n\nGEMsembler tool for assembling and comparing several types of Genome-Scale Metabolic\nModels. \n\n**THIS IS A BETA VERSION! BUGS CAN BE EXPECTED**\n\n## Installation\n\nInstall with the following command:\n```\npip install gemsembler\n```\n\n**Note:** you also have to install BLAST in advance.\n\n## Usage\n\nInput models have to be COBRApy readable files. And models need to be\nparticular type. Currently models made by CarveMe (carveme), ModelSEED\n(modelseed), gapseq (gapseq) and models downloaded from AGORA VMH database\n(agora) are supported. Custom type is coming soon. Genomes, from which the\nmodels are built will allow to convert and assemble genes as well.\nFirst, we import gemsembler and get the path to data files:\n```\nfrom gemsembler import GatheredModels, lp_example, get_model_of_interest\n```\nlp_example is a list with input models and related inforamtion such as model type, corresponding genome and so on.\n```\nlp_example = [\n dict(\n model_id=\"curated_LP\",\n path_to_model=files(LP) / \"LP_iLP728_revision_data_met_C_c.xml.gz\",\n model_type=\"carveme\",\n path_to_genome=files(LP) / \"LP_protein_fasta.faa.gz\",\n ),\n dict(\n model_id=\"cauniv_LP\",\n path_to_model=files(LP) / \"LP_CA1.xml.gz\",\n model_type=\"carveme\",\n path_to_genome=files(LP) / \"LP_protein_fasta.faa.gz\",\n ),\n dict(\n model_id=\"cagram_LP\",\n path_to_model=files(LP) / \"LP_CA2.xml.gz\",\n model_type=\"carveme\",\n path_to_genome=files(LP) / \"LP_protein_fasta.faa.gz\",\n ),\n dict(\n model_id=\"msgram_LP\",\n path_to_model=files(LP) / \"LP_MS2.sbml.gz\",\n model_type=\"modelseed\",\n path_to_genome=files(LP) / \"LP_protein_fasta.faa.gz\",\n ),\n dict(\n model_id=\"agora_LP\",\n path_to_model=files(LP) / \"LP_WCFS1_agora.xml.gz\",\n model_type=\"agora\",\n path_to_genome=files(LP) / \"LP_WCFS1.fasta.gz\",\n ),\n]\n```\n\nFirst stage is the creation of gathered models, a class, that performs\nconversion and contains results of all stages:\n```\ngathered = GatheredModels()\nfor model in lp_example:\n gathered.add_model(**model)\ngathered.run()\n```\nSecond stage is actual assembly of supermodel from the in formation in gathered\nmodels. User has to provide output folder. And for gene conversion user hast\nprovide either final genes in fasta. Then all gene will be converted to ids in\nthese files. Or if user provides NCBI assembly ID for his organism of interest,\ncorresponding genome will be downloaded automatically and all genes will be\nconverted to the locus tags of the organism.\n```\nsupermodel_lp = gathered.assemble_supermodel(\"./gemsembler_output/\", assembly_id = \"GCF_000203855.3\")\n```\nAfter supermodel is assembled different comparison methods can be run\n```\nsupermodel_lp.at_least_in(2)\n```\nAnd results of comparison can be extracted as typical COBRApy models\n```\ncore2 = get_model_of_interest(supermodel_lp, \"core2\", \"./gemsembler_output/LP_core2_output_model.xml\")\n```\n",
"bugtrack_url": null,
"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 copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
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