What it does
Magnifier lets researchers manage and run a four-step bioinformatics pipeline through a web interface.
Users register, create projects, upload a gene expression file and one or more VCF files per experiment,
trigger the pipeline, and download the final contingency table when it finishes.
The pipeline runs asynchronously in the background so users can track progress in real time.
Analysis pipeline
Four Python scripts run sequentially. Each script takes the output of the previous one as input.
Script 1
DEA
Differential Expression Analysis on the uploaded gene expression file.
Script 2
VEP
Ensembl Variant Effect Predictor processing on uploaded VCF files.
Script 3
Join
Joins DEA results with VEP annotations into a unified table.
Script 4
Contingency
Generates the final contingency table from the joined results.
Architecture
→
magnifier_python
Flask frontend — login, dashboard, project and experiment management
→
magnifier-api
Flask REST API backend — pipeline execution, file handling, database
→
Celery + Redis
Async task queue — pipeline runs in background without blocking the server
→
SQLAlchemy ORM
Database layer — Users → Projects → Experiments → Results hierarchy
Python 3
Flask
SQLAlchemy
Celery
Redis
PostgreSQL
Google OAuth
uWSGI