AutoBarcoder

Demultiplex and cluster RNA barcodes from 96-well-plate sequencing reads — in your browser or terminal.

AutoBarcoder takes a pile of sequencing reads, sorts them back into 96 wells using your row/column barcodes, clusters the variable RNA barcode between two flanking sequences, and produces a per-well summary (TXT + PDF) of the top barcode variants. It handles single plates and multi-plate runs.

What it does

┌──────────────────┐   ┌──────────────────┐   ┌──────────────────┐   ┌──────────────────┐
│ Sequencing reads │ → │  Row+Column tag  │ → │  Extract between │ → │  Per-well TXT +  │
│  (FASTQ / TXT)   │   │   demultiplex    │   │  flanks; cluster │   │   PDF + JSON     │
└──────────────────┘   └──────────────────┘   │   by Levenshtein │   └──────────────────┘
                                              └──────────────────┘

Inputs you provide:

• Sequencing data (FASTQ or plain-text — every line containing both row & column tags is scanned).
• Row barcodes + column barcodes (one per line).
• 5′ and 3′ flanking sequences that bracket the variable RNA barcode.
• Expected barcode length and a Levenshtein edit-distance tolerance for clustering.

Outputs you get:

• summary.txt — per-well top-3 barcodes and percentages.
• results.pdf — one bar chart per well.
• JSON results via the web API (programmatic access).
• Optional GraphPad-Prism-friendly CSVs for selected wells.

Quick start (web UI — recommended)

The fastest way to try AutoBarcoder is the new browser UI.

git clone https://github.com/abachu2005/AutoBarcoder-OS-.git
cd AutoBarcoder-OS-
python3 bin/autobarcoder-setup       # interactive wizard: venv + deps + smoke test
bash webapp/run.sh                   # serve at http://127.0.0.1:8000

Then open http://127.0.0.1:8000 and click “Try with sample data” to see a working analysis on a synthetic 4×4 plate.

Quick start (desktop GUI)

pip install -r requirements.txt
python main.py

The Tkinter app provides the same controls as the web UI in a single window, useful when you can't run a local server.

Quick start (Python API)

from barcodes.processing import process_single_plate_for_reads

with open("reads.fastq") as f:
    results = process_single_plate_for_reads(
        "summary.txt", "results.pdf", f.readlines(),
        start_text="CAGCTG", end_text="GGATCC",
        length_threshold=25, distance_threshold=2,
        rows=["AAACGT", "AATTGG"], columns=["CCGTAA", "CCGGTT"],
        expected_len=20,
    )

Repository layout

.
├── barcodes/             # core analysis library (reading, clustering, analysis, processing)
├── gui/                  # Tkinter desktop GUI
├── webapp/               # FastAPI + HTML/JS web UI
│   ├── backend/main.py
│   └── frontend/index.html
├── bin/autobarcoder-setup# interactive setup wizard
├── sample_data/          # tiny synthetic dataset for the demo button
├── viz.py                # publication-style clustering web renderer (standalone)
├── diagnostics.py        # diagnostic motif-counting GUI
├── main.py               # Tkinter launcher
└── requirements.txt

Configuration reference

Output catalogue

• summary.txt — one line per well: R{row}C{col}: G<seq> (xx.xx%), G<seq> (xx.xx%), … or CONTAMINATED (no barcodes).
• results.pdf — bar chart per well, top 3 clusters.
• prism_ready/ (if prism_export_wells is set in the API) — Wide- and long-format CSVs.
• Web jobs additionally expose JSON at /api/jobs/{id}/results and downloadable artifacts.

License

MIT — see LICENSE.