Make and analyse many metabolic models simultaneously in ScrumPy.
Tuatara allows for complete genome assemblies of different strains of your model's species to be adapted into metabolic models. From the publically available tools prokka and Roary, translate gene presence/absence results into reaction presence/absence with tuatara. Instead of creating and curating more than one metabolic model of closely related strains, tuatara stores and handles all the accessory reactions. Meaning only a single metabolic model for a species is required. Thus, the user only needs to curate the accessory reactions.
^ (top) GSM, (middle) database organisms, (bottom) bacteria samples. All from the same species of bacteria
To help with the curation of metabolic models, tuatara comes with tools for rapidly identifying unwanted elements in a model. It also includes assisting tools for visualising linear programmes and analysis of models.
- Creating pseudo metabolic models of strains
- Containers for storing and dealing with many metabolic models
- Rapid searching for unwanted items in metabolic models
- Visualisation of linear programmes
- Multiple organism database handling and comparing
- ScrumPy (3.0-alpha)
- Python (3.6.9 and higher)
- pandas (1.1.5)
- numpy (1.19.5)
- flashtext (2.7)
- matplotlib (3.3.4)
- networkx (2.5)
- PyYAML (5.4.1)
- Seaborn
Building your first nest.
>>> import tuatara as tua
>>> inputs = tua.Inputs(
roary="filepath",
model="modelA",
databases={
"sampleE" : "databaseA",
"sampleF" : "databaseB"
},
fp="filepath",
annots="filepath",
locustags="filepath"
)
>>> inputs.rename({
"sampleX" : "sampleA",
"sampleY" : "sampleB"
})
>>> inputs.drop("sampleB")
>>> inputs.samples
"sampleA, sampleC, sampleD"
>>> inputs.databases
"sampleE, sampleF"
>>> nest = tua.BuildNest(inputs)
# Output