PIPA - Pipeline for Microbial Genomic Analysis¶
PIPA is an integrated platform for microbial genomic analysis that supports Illumina, Nanopore, and PacBio sequencing data. It provides a web interface with a Flask backend that orchestrates bioinformatics tools for read trimming, genome assembly, gene prediction, and reporting.
Pipeline Overview¶
Raw Reads (FASTQ) Assembled Genome (FASTA)
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[Trimming] |
Trim Galore / Porechop_ABI |
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[Assembly] |
SPAdes / Canu / Flye / Unicycler |
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+---------------------------------------+
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[Annotation] <-- 47 tools in 5 categories:
General: Prokka, Bakta, MLST, Barrnap,
tRNAscan-SE, EggNOG, KOFAM
Quality: BUSCO, CheckM, QUAST
Resistance: Abricate, AMRFinderPlus, mcroni
Mobile: PlasmidFinder, MOB-suite, Phigaro,
PhiSpy, CRISPRCasFinder, DefenseFinder
Typing: 27 organism-specific tools
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[Report]
KEGG-decoder
Two Workflows¶
Annotation Only¶
Upload an assembled genome (FASTA) and select which annotation tools to run. Ideal when you already have an assembly and want to quickly annotate it.
Full Pipeline¶
Upload raw sequencing reads (FASTQ) from Illumina, Nanopore, or PacBio. PIPA will trim, assemble, and annotate automatically.
Features¶
- 47 bioinformatics tools organized in 5 categories
- Generic organism support - works with any bacterial genome
- 27 organism-specific typing tools - auto-enabled based on genus/species
- Selectable tools - choose exactly which annotation tools to run
- Desktop application - native apps for macOS, Windows, Linux with Docker auto-setup
- Async pipeline - submit jobs and monitor progress in real-time
- REST API - programmatic access to all pipeline functionality
- Docker support - all tools pre-installed in
lcerdeira/pipaimage
Quick Start¶
Desktop App (recommended)¶
- Install Docker Desktop
- Download PIPA from GitHub Releases
- Launch — backend starts automatically
Docker (standalone)¶
docker run -d --name pipa -p 5000:5000 -v pipa-data:/data lcerdeira/pipa:latest
# API available at http://localhost:5000
Conda¶
conda env create -f environment.yml
conda activate pipa
cd back-end && flask run --host 0.0.0.0 --port 5000
See Installation for detailed instructions.