GPR Analysis – Django App

A Django application for Gene-Protein-Reaction (GPR) rule analysis of metabolic models. It uses DIAMOND/BLAST sequence alignment to map genome genes to protein complexes and automatically generates GPR rules for SBML metabolic models.

Features

• Upload a metabolic model (SBML/JSON) and a genome FASTA file
• DIAMOND-accelerated protein sequence search against a curated complexes database
• Automatic GPR rule generation for reactions
• Interactive resolution of ambiguous gene-to-complex assignments
• Download of updated metabolic models with new GPR rules
• Session-based job tracking (works for both authenticated and anonymous users)
• Background queue worker for processing jobs

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Prerequisites

System packages

Tool	Purpose	Install
Python ≥ 3.9	Runtime	System package manager
DIAMOND	Fast protein alignment (primary)	sudo apt-get install diamond-aligner
NCBI BLAST+	Protein alignment (fallback)	sudo apt-get install ncbi-blast+

Verify both are on your PATH:

diamond version
blastp -version

Python packages

Install from the included requirements file:

pip install -r requirements.txt

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Installation

1. Clone the repository and install dependencies

git clone <repo-url>
cd GPR
pip install -r requirements.txt

The project structure:

├── manage.py
├── requirements.txt
├── run_unified_worker.py   # background job queue worker
├── config/                 # Django project settings
│   ├── settings.py
│   ├── urls.py
│   ├── wsgi.py
│   └── asgi.py
├── GPR/                    # the GPR analysis app
│   ├── admin.py
│   ├── forms.py
│   ├── models.py
│   ├── services.py
│   ├── views.py
│   ├── urls.py
│   ├── migrations/
│   ├── templates/
│   └── templatetags/
├── data/                   # reference databases
│   ├── complexes_blast_db/
│   │   ├── complexes.dmnd
│   │   └── complexes.fasta
│   └── gpr_reference/
│       ├── complex_stoichiometry.pkl
│       ├── complex_metadata.pkl
│       └── complexes_list.pkl
└── media/                  # upload & output directory
    ├── gpr_genomes/
    ├── gpr_models/
    └── sbml/

2. Run migrations

python manage.py migrate

3. Load complex metadata into the database

The ComplexMetadata table should be populated for the ambiguity-resolution UI to show complex names and descriptions:

python manage.py shell

import pickle
from GPR.models import ComplexMetadata

with open('data/gpr_reference/complex_metadata.pkl', 'rb') as f:
    metadata = pickle.load(f)

for complex_id, description in metadata.items():
    ComplexMetadata.objects.update_or_create(
        complex_id=complex_id,
        defaults={'name': complex_id, 'description': description}
    )

4. Start the background queue worker

Run the worker in a separate terminal (or as a systemd service):

python run_unified_worker.py

The worker:

• Polls the database every 5 seconds for queued jobs
• Runs up to 4 jobs concurrently
• Writes a PID file to media/queue_worker.pid
• Handles SIGTERM/SIGINT gracefully

5. Start the development server

DJANGO_DEBUG=true python manage.py runserver

Note: DEBUG defaults to False. You must set DJANGO_DEBUG=true for the development server to serve media files (uploaded genomes, models, results).

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Usage

1. Navigate to /GPR/ in your browser
2. Enter an organism name, upload a metabolic model (.xml, .sbml, or .json) and a genome FASTA file (.fasta, .fa, .fna)
3. The job is queued and processed by the background worker
4. View results at /GPR/job/<id>/ — BLAST hits, GPR rules, complex assignments
5. If ambiguous cases exist, resolve them at /GPR/job/<id>/resolve-ambiguities/
6. Download the updated model with the new GPR rules

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What's Included

This repository ships as a ready-to-run Django project:

Item	Status
manage.py	Included
config/settings.py	Included — pre-configured with 'GPR' in INSTALLED_APPS and MEDIA_ROOT
config/urls.py	Included — GPR routes and media serving pre-configured
run_unified_worker.py	Included — background queue worker
requirements.txt	Included
Static file collection	Not needed — templates use Bootstrap 5 via CDN

Recommended production setup

• Use a process manager (systemd, supervisor) for the queue worker instead of running it in a bare shell
• Use PostgreSQL instead of SQLite for concurrent access (select_for_update is used for queue locking)
• Serve media/ via Nginx/Apache in production rather than Django's static() helper
• Set appropriate FILE_UPLOAD_MAX_MEMORY_SIZE in settings for large genome files

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