TFBPShiny

A Shiny web application for exploring transcription factor binding and perturbation data from the Brent Lab yeast collection.

Table of contents

• Resource Requirements
• Quick start
  • Install
  • Build the database, then run the app
• Contributing
  • Setup
  • Plotly JS bundle
  • Environment variables
  • Running the app
  • Running tests
  • Code quality
  • Branching
• Further documentation

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Resource Requirements

This app requires the following minimum resources to run:

• 4GB storage on disk
• 8GB RAM (10GB or more is recommended for better performance)

Quick start

If you wish to keep the app separated from your local environment, you should first create a virtual environment. You can do this with venv. cd to the directory where you want the virtual environment to be created, and run:

python -m venv tfbpshiny_env
source tfbpshiny_env/bin/activate

Install

python -m pip install tfbpshiny

Build the database, then run the app

The app reads a single pre-built DuckDB file. Build it once (this pulls every dataset from HuggingFace and runs the cross-dataset analyses; about twenty minutes), then launch. The collection config ships inside the installed package:

CONFIG=$(python -c "import pathlib, tfbpshiny; print(pathlib.Path(tfbpshiny.__file__).parent / 'brentlab_yeast_collection.yaml')")
python -m tfbpshiny materialize --config "$CONFIG" --output brentlab_yeast.duckdb
python -m tfbpshiny launch --db-path brentlab_yeast.duckdb

Re-run materialize whenever the upstream datasets change. See docs/development.md for what the build produces.

To install the latest development version from GitHub, use:

python -m pip install git+https://github.com/BrentLab/tfbpshiny@dev

For shinyapps.io / Posit Connect deployment instructions, see docs/development.md.

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Contributing

Setup

git clone https://github.com/BrentLab/tfbpshiny.git
cd tfbpshiny
poetry install
pre-commit install
# First-time Playwright setup (required for E2E tests)
poetry run playwright install chromium

Plotly JS bundle

The app loads Plotly from a local bundle (tfbpshiny/www/plotly-3.5.0.min.js) rather than a CDN to avoid race conditions when multiple outputs initialize simultaneously. This file is gitignored due to its size (~4.8 MB). After cloning, download it once:

curl -fsSL https://cdn.plot.ly/plotly-3.5.0.min.js \
    -o tfbpshiny/www/plotly-3.5.0.min.js

If the plotly Python package is upgraded, check the new JS version it expects:

python -c "
import re, plotly.graph_objects as go
from plotly.io import to_html
m = re.search(r'plotly-([\d.]+)\.min\.js', to_html(go.Figure(), include_plotlyjs='cdn'))
print(m.group(0))
"

Then download the matching version and update the src in tfbpshiny/app.py.

Environment variables

HF_TOKEN is read by materialize (or pass --token) and is only needed for private HuggingFace datasets. HF_HOME controls where the downloads are cached. The app itself reads TFBPSHINY_DB_PATH, which launch --db-path sets for you.

Running the app

poetry run python -m tfbpshiny materialize \
    --config tfbpshiny/brentlab_yeast_collection.yaml \
    --output tfbpshiny/brentlab_yeast.duckdb   # once; the launch default path
poetry run python -m tfbpshiny --log-level DEBUG launch \
    --port 8010 --host 127.0.0.1 --debug

Running tests

poetry run pytest tests/unit/      # unit tests
poetry run pytest tests/e2e/       # end-to-end
poetry run pytest                   # all tests

Code quality

pre-commit run --all-files

Branching

1. Switch to dev: git switch dev
2. Branch from dev — not main: git switch -c my-feature
3. Keep branches small and focused to make review easier
4. Rebase onto dev periodically: git rebase dev
5. When ready, open a pull request targeting the BrentLab dev branch — not main

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Further documentation

• docs/development.md: architecture, the build, and deployment
• docs/materialized_db_schema.md: every table in the database
• docs/sql_operations.md: the SQL the app runs
• Page guides: Dataset selection, Binding, Perturbation, Comparisons
• CHANGELOG.md