GEMsembler

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")

Metadata

Release files for gemsembler 0.11.16

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gemsembler 0.11.16
File	Size	Uploaded
gemsembler-0.11.16.tar.gz	5.0 MB	Jun 29, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for gemsembler 0.11.16
File	Interpreter	ABI	Platform	Reset
gemsembler-0.11.16-py3-none-any.whl 5.0 MB Jun 29, 2026	Python 3	none	any	Details

Total release size: 10.0 MB

Release files / gemsembler-0.11.16.tar.gz

Download URL	gemsembler-0.11.16.tar.gz
Size	5.0 MB
Tags	Source
SHA-256 checksum How to use checksums	`884d5f4d24ff237735e8b9ee214f3d389d25fce6b5afb8626d9a0b64af517ad2`
BLAKE2b-256 checksum How to use checksums	`2200ee82bb0979624dc626d992b83574192f16df209134b21d8d54968d36e446`
Upload date	Jun 29, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.13`

Release files / gemsembler-0.11.16-py3-none-any.whl

Download URL	gemsembler-0.11.16-py3-none-any.whl
Size	5.0 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`5fe24524fde060ed2fd68dd1e337224d8f912dc68814c9a5fd9306d9d9df0228`
BLAKE2b-256 checksum How to use checksums	`092a292709b3f1ee640f7a8114d08edba1fe901b25f1e3ce75c22a423e6a87e5`
Upload date	Jun 29, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.13`