A novel way to gapfill metabolic models

Installation instructions

To run the dnngior gapfiller, the Gurobi solver is mandatory.

pip install gurobipy

To use gurobi, you need a license. If you are an acedemic, you may get a license for free.

Once you have successfully installed gurobi, you are ready to install the dnngior gapfiller.

pip install dnngior

Optionally, you may need to also get Tensorflow (or through conda) in case you would like to use the NN_Trainer.

How to use

Gapfilling models is done using the Gapfill class:

import dnngior.gapfill_class.Gapfill  
Gapfill(path_to_model)

You may find examples of gap-filling a genome scale reconstruction (GEM) with dnngior with a complete or a defined medium in this example notebook. dnngior can gapfill both ModelSEED and BiGG models, to gapfill BiGG models you need to specify modeltype.

Gapfill(path_to_BiGG_model, dbType='BiGG')

Custom Networks

By default dnngior uses an universally trained network capable of accurate predictions under most circumstances. If desired, it is possible to change the Neural Network you want to use during gapfilling:

Gapfill(path_to_model, trainedNNPath=path_to_NN)

You can train your own Neural Network following this tutorial: example training NN.

Alternatively you can find additional custom Neural Networks for several taxonomic groups: Custom Networks. Upon request additional specially trained networks can be made available for specific biomes or taxonomic groups.

Batch CLI

You can use this CLI script to automate the workflow of creating metabolic models from protein sequence data assuming default settings. This allows easier generation of larger number of models.

You can run it in two modes:

Option 1: From FASTA files python fasta2model_CLI.py -f /path/to/fasta_folder -o /path/to/output [--medium /path/to/media_file]

The script will create MSGenome objects from each .faa file, optionally annotate them via RAST, build base metabolic models, and perform gap-filling based on complete medium or your specified medium if provided.

Option 2: From existing base models python fasta2model_CLI.py -m /path/to/base_models -o /path/to/output [--medium /path/to/media_file]

Skip base model construction and only perform gap-filling on pre-existing .xml model files.

Base models are saved to output_folder/base_models/, gap-filled models to output_folder/gapfilled_models/, and gap-filling statistics are logged in a gf_data.tsv file showing reaction counts per model. The optional -e flag lets you specify growth conditions via a medium file.

See also python fasta2model_CLI.py -h

License

Please see License

Cite

The paper that accompanies the tool can be found here:
https://www.cell.com/iscience/fulltext/S2589-0042(24)02574-4