PIPA is an integrated platform for microbial genomic analysis that supports Illumina, Nanopore, and PacBio sequencing data. It provides a web interface and desktop application with a Flask backend that orchestrates bioinformatics tools for read trimming, genome assembly, gene prediction, and reporting.
Full Documentation | GitHub Repository | Download Desktop App
| Stage | Tools | Description |
|---|---|---|
| Trimming | Trim Galore, Porechop_ABI | Quality trimming of Illumina and Nanopore reads |
| Assembly | SPAdes, Canu, Flye, Unicycler | Genome assembly from short and long reads |
| Annotation | 48 tools across 5 categories (see below) | Gene annotation, typing, resistance, virulence, and more |
| Report | KEGG-decoder | Functional pathway visualization |
| Category | Tools |
|---|---|
| General Annotation | Prokka, Bakta, MLST, Barrnap, tRNAscan-SE, EggNOG-mapper, KOFAM |
| Assembly Quality | BUSCO, CheckM, QUAST |
| Resistance & Virulence | Abricate (NCBI), Abricate (VFDB), AMRFinderPlus, mcroni |
| Mobile Elements & Defense | PlasmidFinder, MOB-suite, Phigaro, PhiSpy, CRISPRCasFinder, DefenseFinder, ISMapper |
| Organism-Specific Typing | Kleborate, staphtyper, TBProfiler, ClermonTyping, ECTyper, emmtyper, GenoTyphi, hicap, HpSuisSero, legsta, LisSero, meningotype, ngmaster, pasty, pbptyper, PneumoCaT, sccmec, SeqSero2, SeroBA, ShigaPass, ShigaTyper, ShigEiFinder, SISTR, spaTyper, SsuisSero, staphopia-sccmec, STECFinder |
Download native installers from the Releases page:
| Platform | File | Architecture |
|---|---|---|
| macOS (Apple Silicon) | PIPA_2.0.0_aarch64.dmg |
M1, M2, M3, M4 |
| macOS (Intel) | PIPA_2.0.0_x64.dmg |
Intel Macs |
| Windows | PIPA_2.0.0_x64-setup.exe |
64-bit Windows 10/11 |
| Linux (Debian/Ubuntu) | pipa_2.0.0_amd64.deb |
Debian-based |
| Linux (Universal) | pipa_2.0.0_amd64.AppImage |
Any 64-bit Linux |
The desktop app requires Docker Desktop to run the bioinformatics backend. On first launch, PIPA automatically pulls the lcerdeira/pipa Docker image (~5 GB compressed) with all 48 analysis tools pre-installed. No manual configuration required.
- Install Docker Desktop
- Download and install PIPA from Releases
- Launch PIPA — the backend starts automatically
The PIPA Docker image is available on Docker Hub:
# Pull and run the backend with all 48 bioinformatics tools
docker run -d --name pipa-backend -p 5000:5000 -v pipa-data:/data lcerdeira/pipa:latest
# The API is now available at http://localhost:5000
curl http://localhost:5000/api/jobsOr clone the repository and build locally:
git clone https://github.com/lcerdeira/Pipa.git
cd Pipa
docker build -t pipa .
docker run -d --name pipa-backend -p 5000:5000 -v pipa-data:/data pipa# Install backend dependencies
cd back-end
pip install -r requirements.txt
flask run --host 0.0.0.0 --port 5000
# In another terminal, install and run the frontend
cd UI/DesktopPIPA
yarn install
npx quasar devOpen http://localhost:8080 in your browser.
For native installation with all bioinformatics tools:
# Create the conda environment
conda env create -f environment.yml
conda activate pipa
# Start the backend
cd back-end
flask run --host 0.0.0.0 --port 5000| Method | Endpoint | Description |
|---|---|---|
POST |
/api/upload |
Upload sequencing files (multipart form) |
POST |
/api/run |
Start pipeline with configuration |
GET |
/api/status/<job_id> |
Get pipeline progress and status |
GET |
/api/results/<job_id> |
Get pipeline results |
GET |
/api/results/<job_id>/files/<path> |
Download result file |
GET |
/api/jobs |
List all jobs |
# Upload files
curl -X POST http://localhost:5000/api/upload \
-F "illumina=@reads_R1.fastq.gz" \
-F "illumina=@reads_R2.fastq.gz" \
-F "illumina_type=paired"
# Start pipeline
curl -X POST http://localhost:5000/api/run \
-H "Content-Type: application/json" \
-d '{"job_id": "abc123", "genus": "Klebsiella", "species": "pneumoniae", "genome_size": "5.5m"}'
# Check status
curl http://localhost:5000/api/status/abc123The pipeline accepts these parameters per run:
| Parameter | Default | Description |
|---|---|---|
genus |
Unknown | Organism genus (used by Prokka, Kleborate) |
species |
unknown | Organism species |
sample_name |
sample | Name for the analysis run |
genome_size |
5m | Estimated genome size (used by Canu) |
input_type |
reads | reads (full pipeline) or assembly (annotation only) |
tools |
[] | Annotation tools to run (e.g., ["prokka", "mlst", "abricate"]) |
Environment variable PIPA_DATA_DIR controls where data is stored (default: ./data).
Pipa/
back-end/ # Flask REST API
app.py # Application entry point
blueprints/ # API and view routes
extensions/ # Pipeline services
services.py # Pipeline orchestrator
services1/ # Individual service modules
tests/ # API tests
UI/DesktopPIPA/ # Desktop & web application
src/ # Vue.js components and store
src-tauri/ # Tauri desktop wrapper (Rust)
docs/ # Documentation (MkDocs)
environment.yml # Conda environment specification
Dockerfile # Container build
docker-compose.yml # Container orchestration
examples/ # Example scripts
cd back-end
pip install pytest
python -m pytest tests/ -vDr Louise Cerdeira - [email protected]
Copyright 2026. GPL-3.0.
If you use PIPA in your research, please cite:
Cerdeira, L. PIPA: Pipeline for Microbial Genomic Analysis. https://github.com/lcerdeira/Pipa