biofilter 4.1.3

Biofilter: cloud-ready biological knowledge system

Biofilter 4

Biofilter 4 is a persistent, entity-centric biological knowledge platform designed to support gene-centric annotation, filtering, and modeling workflows through a unified and extensible data architecture.

This branch (biofilter3r) contains the active development of Biofilter 4, representing a major evolution of the Biofilter framework with a redesigned schema, modern ETL architecture, and multiple interaction layers.

📚 Documentation:
👉 https://biofilter.readthedocs.io/en/latest/

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Quick Start

Install via pip:

pip install biofilter
biofilter --help

Connect to a database (existing instance or local) and run your first report:

export DATABASE_URL="postgresql+psycopg2://user:password@host:5432/biofilter_prod"
biofilter report list
biofilter report run --report-name etl_status --output etl_status.csv

From Python:

from biofilter import Biofilter

bf = Biofilter()
df = bf.report.run("entity_filter", input_data=["BRCA1", "TP53", "APOE"])
df.head()

For Docker, source install, or bootstrapping a local database, see the Getting Started guide.

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What is Biofilter 4?

Biofilter 4 provides a persistent, versioned biological knowledge base that replaces traditional file-based annotation workflows with a reusable, query-driven platform.

Instead of repeatedly generating transient annotation files, Biofilter 4 enables users to:

• ingest curated biological knowledge once,
• store it in a normalized, entity-based schema,
• reuse and query that knowledge across analyses, projects, and environments.

Biofilter 4 is designed to support both exploratory research and production-scale workflows.

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Core Concepts: Entities, Domains, and Relationships

Biofilter organizes biological knowledge around three core concepts:

• Entities

  • Canonical biological objects (for example Gene, Variant, Disease, Protein, Pathway).

• Domains

  • Functional/omics contexts used to structure and interpret entities and their links.

• Entity Relationships

  • A relational layer that connects entities across domains and behaves like a graph traversal surface while staying in a SQL-native environment.

This design lets users recover cross-omics relationships and reuse them directly in reports for:

• annotation workflows,
• filtering and prioritization workflows,
• relationship-driven analyses that support downstream statistical modeling.

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Key Features

• Entity-centric data model

  • Canonical entities (Gene, Variant, Disease, Protein, Pathway, etc.)
  • Rich alias and cross-reference support

• Persistent knowledge layer

  • Versioned ETL packages
  • Full provenance tracking by data source and load

• Modular ETL architecture

  • Data Transformation Packages (DTPs)
  • Explicit separation of master data and relationships

• High-performance ingestion

  • Managed indexing strategy
  • Optimized for large-scale sources (e.g. dbSNP, UniProt)

• Multiple interaction layers

  • Python API
  • ORM-based data access
  • Reusable Reports
  • Command-line interface (CLI)

• Multi-database support

  • SQLite (local development)
  • PostgreSQL (production and large-scale deployments)

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Architecture Overview

At a high level, Biofilter 4 consists of:

• ETL Layer

  • Ingests external biological sources into a normalized schema
  • Tracks execution via ETL Packages

• Core Schema

  • Entity, Alias, Relationship, and Domain Master tables
  • Designed for extensibility and long-term evolution

• Data Access Layer

  • ORM-backed, Python-first access to the knowledge base
  • Foundation for reports and advanced analysis

• Report Layer

  • Curated, reusable biological queries
  • Standardized outputs as pandas DataFrames

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Repository Structure (simplified)

biofilter/
├── alembic/                   # Database migrations
├── api/
│   └── cli/                   # CLI commands and entrypoints
├── core/
│   ├── components/            # db, etl, report, settings components
│   └── settings_manager.py
├── modules/
│   ├── db/                    # ORM models, seeds, schema
│   ├── etl/                   # ETL framework and DTPs
│   ├── io/                    # Input/output utilities
│   └── report/                # Report framework and reports
├── utils/                     # Shared helpers
└── biofilter.py               # Python API facade

docs/
└── source/                    # Sphinx documentation source

notebooks/
└── Templates/                 # Ready-to-use report tutorials

tests/
├── unit/
└── integration/

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Documentation

The full User Guide and Developer Guide are hosted on Read the Docs:

📖 https://biofilter.readthedocs.io/en/latest/

The documentation covers:

• Installation and setup
• Data sources and ETL design
• Writing DTPs
• Managed indexes
• Entity and alias registration
• Data access and report internals
• Writing and extending reports
• Developer tooling and project structure

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Resources

• 🤖 GPT Assistant — conversational guidance for picking and using reports: Biofilter 4 Assistant
• 📓 Notebook tutorials — ready-to-run examples for every report: notebooks/Templates/
• 📋 Report Catalog — full index of available reports with descriptions: Read the Docs

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Run with Docker (Container)

Biofilter 4 can be executed as an application-only container, using an external database via DATABASE_URL.

Build from this repository:

docker build -t biofilter:bf4 -f docker/Dockerfile .

Run CLI with external DB:

docker run --rm \
  -e DATABASE_URL="postgresql+psycopg2://user:password@host:5432/biofilter_prod" \
  biofilter:bf4

Run a report and save output to your local machine:

docker run --rm \
  -e DATABASE_URL="postgresql+psycopg2://user:password@host:5432/biofilter_prod" \
  -v "$(pwd)/outputs:/workspace/outputs" \
  biofilter:bf4 \
  biofilter report run \
    --report-name etl_status \
    --output /workspace/outputs/etl_status.csv

Open an interactive shell in the container:

docker run --rm -it \
  -e DATABASE_URL="postgresql+psycopg2://user:password@host:5432/biofilter_prod" \
  -v "$(pwd):/workspace" \
  --entrypoint /bin/bash \
  biofilter:bf4

For full container documentation (publishing, multi-arch, GitHub Actions), see:

• docker/README.md

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Status

• Current version: 4.1.2
• Schema: Entity-centric, versioned (4.1.x)
• ETL: Modular DTP-based ingestion
• Stability: Actively evolving; APIs and schema may continue to change between minor releases

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Contributing

Contributions, feedback, and design discussions are welcome.

When contributing:

• Follow existing architectural patterns (Entities, DTPs, Reports).
• Keep provenance and reproducibility as first-class concerns.
• Prefer ORM-based logic over raw SQL when possible.
• Document new features in the appropriate section of the docs.

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License

MIT License. See LICENSE.

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Acknowledgements

Biofilter builds on years of development and scientific usage across multiple generations of the framework. Biofilter 4 represents a continuation of this work, redesigned to support modern data volumes, richer biological relationships, and long-term sustainability.