Mycelium is a high-performance knowledge discovery platform designed to map the intricate networks of biological data. Just as mycelium forms the underlying nervous system of the natural world, this platform unifies disparate datasets into a cohesive, navigable graph architecture—empowering researchers to uncover hidden relationships and accelerate the pace of scientific insight.
Roadmap:
- Zero-configuration to start developing
- Provide alternative tab/panel implementation
- Correct sqlachemy models
- Reactive file indexing and annotating
- Unified tooltips
- Angular PDF.js integration
- Drop jQuery dependency
- Observable files (pararrel edits)
- Add automated tests
- Automated itegration/deployment
| Feature / Improvement | SBRG/lifelike | Mycelium |
|---|---|---|
| Dev environment | Manual setup required | Zero-config via VS Code Dev Container / GitHub Codespaces |
| Angular version | v9 | v14 |
| Tab/panel implementation | Custom component | URL-encoded named router outlets (route-with-dynamic-outlets) |
| PDF viewer library | pdfjs-dist 2.9.359 | pdfjs-dist 4.2.67 (CVE-2024-4367 fixed) |
| Office file support | ❌ | ✅ Open and view Office files (.docx, .xlsx, .pptx, .xls, .ppt, .odt, etc.) |
| Protein structure viewer | ❌ | ✅ Mol* viewer for .pdb, .cif, .mmcif |
| Code/text viewer | ❌ | ✅ CodeMirror 6 read-only viewer with syntax highlighting |
| Folder-level annotation config | ❌ | ✅ .annotations JSON files with inheritance/overrides |
| jQuery dependency | ❌ (jquery, jquery-ui, qtip2) | ✅ Removed — replaced with native DOM APIs & Bootstrap 5 Popover |
| Python linting | ❌ | ✅ ruff (E/F rules) across all Python services |
| Comprehensive linting | ❌ | ✅ MegaLinter with SARIF upload & PR annotations |
| CI/CD pipelines | ❌ | ✅ GitHub Actions: tests, Docker build/publish, CodeQL, Dependabot auto-merge |
| Automated UI tests | ❌ | ✅ Angular unit specs for core UI components |
| Database migrations | 100+ incremental Alembic files | Single squashed baseline migration |
| d3 version | v5 | v7 |
| Flask version | 2.x | 3.x |
| Bootstrap | 5 (with import issues) | 5 (fixed SCSS architecture) |
| Security hardening | — | Patched pdfjs-dist CVE-2024-4367, cryptography bumps |
| Copilot / AI dev support | ❌ | ✅ Copilot coding agent instructions & auto-fix workflow |
Mycelium started as a fork of Lifelike that aims to provide a simple, yet powerful platform for turning structured and unstructured data from a variety of sources into a single, coherent and explorable knowledge graph.
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Textual legend: "This project uses code provided by Lifelike.bio"
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Embeded Lifelike logo image:
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Link to Lifelike public GitHub repository: https://github.com/SBRG/lifelike
The easiest way to get started and run a fully functional development environment of Mycelium is to clone this repository and run the make up command:
git clone https://github.com/Skitionek/Mycelium.git
cd Mycelium
make upShell into appserver and run:
./bin/dev-db-setup
flask seedThis will take a few minutes to complete, after which you can start using Mycelium by pointing your browser to http://localhost:8080.
You can log in using the default admin user admin@example.com and password password.
To reduce startup time for new Codespaces, enable GitHub Codespaces prebuilds for this repository:
- Open your repository on GitHub.
- Go to Settings -> Codespaces.
- In Prebuild configurations, click Set up prebuild.
- Choose branch
main(and any long-lived development branches you use). - Select
.devcontainer/devcontainer.jsonas the dev container configuration. - Enable updates on push (recommended) so the prebuild stays warm after merges.
This repository now runs .devcontainer/post-create.sh during container creation, which builds and starts the development stack ahead of time. When prebuilds are enabled, that setup work happens during prebuild generation instead of when each developer starts a fresh Codespace.
You can see more details about how to deploy Mycelium in a production environment, or customize the development installation in the following sections.
Mycelium organizes content into projects. A project is a filesystem-like collection of resources either uploaded by users or generated by Mycelium based on other resources. Those resources can be all kinds of data, including structured data like spreadsheets, unstructured data like PDF files, images, or text documents.
Mycelium structures knowledge around domains. A domain is a collection of semantically related entities belonging to a field of study.
Annotations are a powerful way to attach context to your data in knowledge Domains, Mycelium automatically annotates all your data with Domain known entities as well as lets you define your own custom annotations.
Domain data sources are annotated and stored in a graph database. A knowledge graph consists of nodes and edges. Nodes are domain entities and edges are relations between entities.
Visualizations are a powerful way to help you to understand the relationships between entities as well as a powerful tool to find new relationships as new data comes in.
Mycelium currently provides the following built-in visualization types:
- Maps
- Enrichment tables
- Sankey diagrams
- Pathway Browser
- Multi-user collaborative workbench
- Powerful search engine
You can run make help to see a list of available commands.
$ make help
usage: make [target]
development:
githooks Set up Git commit hooks for linting and code formatting
docker:
up Build and run container(s) for development. [c=<names>]
images Build container(s) for distribution.
status Show container(s) status. [c=<names>]
logs Show container(s) logs. [c=<names>]
restart Restart container(s). [c=<names>]
stop Stop containers(s). [c=<names>]
exec Execute a command inside a container. [c=<name>, cmd=<command>]
test Execute test suite
down Destroy all containers and volumes
reset Destroy and recreate all containers and volumes
diagram Generate an architecture diagram from the Docker Compose files
helm:
helm-lint Run helm lint on Mycelium chart
helm-dependency-update Install or update chart dependencies
helm-schema-gen Generate Helm chart values JSON schema
helm-docs Generate Helm chart README docs
helm-package Generate Mycelium helm chart package
helm-install Install or upgrade Mycelium chart
helm-install-single-node Install or upgrade Mycelium chart using the single-node example values
other:
help Show this help.
Mycelium is a distributed system comprised of the following components:
flowchart TD
frontend[Frontend Angular SPA]
appserver[Appserver Flask API]
cache_invalidator[Cache invalidator Task runner]
statistical_enrichment[Statistical enrichment Flask service]
elasticsearch[(Elasticsearch)]
neo4j[(Neo4j)]
pdfparser[PDFParser]
postgres[(PostgreSQL)]
redis[(Redis)]
frontend --> appserver
appserver --> pdfparser
appserver --> statistical_enrichment
appserver -. depends on .-> elasticsearch
appserver -. depends on .-> neo4j
appserver -. depends on .-> postgres
cache_invalidator -. depends on .-> neo4j
cache_invalidator -. depends on .-> redis
statistical_enrichment -. depends on .-> neo4j
statistical_enrichment -. depends on .-> redis
- Appserver. Backend API service, written in Python using the the Flask framework.
- Client. Frontend Single Page Application, written in Typescript using the Angular framework.
- Statistical enrichment. Statistics generation microservice, written in Python using the the Flask framework.
- Cache invalidator. Recurrent task runner for bulk large computations and cache data management, written in Python.
- Graph data migrator. Utility service for migrating and versioning knowledge graph database, using the Liquibase database migration tool.
- PostgreSQL as a RDBMS.
- Neo4j as a graph database.
- Elasticsearch as a full-text search engine.
- Redis as a key-value cache store.
- PDFParser as a document parsing library.
- Sendgrid as an email messaging service.
Mycelium licensing and upstream notice are described in LICENSE.
See CHANGELOG.md for a history of changes.