Migenpro

Getting started

Pull the git repo:

git pull git@gitlab.com:pig-paradigm/migenpro.git
cd migenpro

Installing the needed dependencies.

A pip requirements.txt file is located in the installation directory which you can install using the following command.

conda create -n migenpro python=3.12.5 pip --file installation/requirements.txt

Annotating genomes using SAPP

To annotate genomes we use a cwltool workflow with SAPP that output the desired genome annotations in hdt files.

cwltool --no-warnings --outdir ./data https://gitlab.com/m-unlock/cwl/-/raw/dev/workflows/workflow_microbial_annotation.cwl --genome_fasta https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/005/845/GCA_000005845.2_ASM584v2/GCA_000005845.2_ASM584v2_genomic.fna.gz

Luckily we have automated this process within the python package.

Training machine learning models

python3 src/main/resources/python/machineLearning.py \
    --featureMatrix ./output/phentype_matrix.tsv \
    --phenotypeMatrix output/protein_domain_matrix.tsv \
    --model_load [Location_of_model] \
    --train
    --predict

Predicting phenotypes with existing models

You can do this through the docker container or from the source code.

1. You will need to obtain a protein domain matrix of the desired genomes you can do this using the java code.
2. For ease of use we will use the python scripts that were made with the following command. The default output directory is "output/mloutput" if desired you can change this using the --output [output_directory_location]

python3 src/main/resources/python/machineLearning.py \
    --featureMatrix ./output/phentype_matrix.tsv \
    --model_load [Location_of_model] \
    --predict

3. Wait for the script to finish and retrieve the results of your prediction from the output directory. There the predictions are given in the following format:

################################################
# Genome # Phenotype # Prediction # Confidence #
# GCA123 # Temprature # mesophilic # 0.96      #
################################################

Recreating the results from the study

The files needed to recreate our results are located in the ./data/phenotype_output folder. We use the previously created protein_domain.tsv and phenotype.tsv files. Run the create_graphs.sh bash script

./recreate.sh