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‎.github/workflows/deploy.yml‎

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name: Deploy to GitHub Pages
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on:
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schedule:
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- cron: 0 5 * * 1
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push:
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branches: [main]
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jobs:
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generate-page:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v6
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- name: Setup conda
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uses: conda-incubator/setup-miniconda@v3
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with:
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channels: conda-forge
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environment-file: environment.yaml
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miniforge-version: latest
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- name: Generate page
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shell: bash -l {0}
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run: |
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python src/scripts/generate_page.py
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python src/scripts/generate_workflow_md.py
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build:
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name: Build Docusaurus
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v6
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with:
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fetch-depth: 0
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- uses: actions/setup-node@v6
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with:
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node-version: 18
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cache: npm
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- name: Install dependencies
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run: npm ci
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- name: Build website
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run: npm run build
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- name: Upload Build Artifact
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uses: actions/upload-pages-artifact@v4
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with:
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path: build
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deploy:
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name: Deploy to GitHub Pages
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needs: build
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permissions:
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pages: write
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id-token: write
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environment:
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name: github-pages
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url: ${{ steps.deployment.outputs.page_url }}
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runs-on: ubuntu-latest
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steps:
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4

‎.github/workflows/test-deploy.yml‎

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name: Test deployment
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on:
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pull_request:
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branches: [main]
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jobs:
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generate-page:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v6
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- name: Setup conda
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uses: conda-incubator/setup-miniconda@v3
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with:
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channels: conda-forge
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environment-file: environment.yaml
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miniforge-version: latest
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- name: Generate page
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shell: bash -l {0}
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run: |
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python src/scripts/generate_page.py
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python src/scripts/generate_workflow_md.py
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test-deploy:
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name: Test deployment
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v6
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with:
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fetch-depth: 0
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- uses: actions/setup-node@v6
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with:
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node-version: 18
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cache: npm
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- name: Install dependencies
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run: npm ci
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- name: Test build website
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run: npm run build

‎.gitignore‎

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# Dependencies
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/node_modules
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# Production
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/build
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# Generated files
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docs/workflows/all_workflows/*.md
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.vscode
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.docusaurus
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.cache-loader
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# Misc
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.DS_Store
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.env.local
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.env.development.local
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.env.test.local
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.env.production.local
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*

‎README.md‎

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# Website
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This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
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### Installation
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```bash
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npm create-docusaurus@latest docusaurus-test classic
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```
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### Local Development
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```bash
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npm start
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```
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This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
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### Build
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```bash
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npm run build
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```
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This command generates static content into the `build` directory and can be served using any static contents hosting service.

‎blog/2025-02-01-welcome/index.md‎

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---
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slug: welcome
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title: Welcome
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authors: [michael]
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tags: [documentation]
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---
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Content to be added.
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<!-- truncate -->

‎blog/authors.yml‎

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michael:
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name: Michael Jahn
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title: Computational Biologist
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url: https://github.com/m-jahn
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image_url: https://github.com/m-jahn.png
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page: true
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socials:
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github: m-jahn

‎blog/tags.yml‎

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documentation:
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label: documentation
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permalink: /documentation
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description: Documentation of workflows

‎docs/projects/intro.md‎

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---
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sidebar_position: 1
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---
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# Introduction
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## Mission
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The Bioinformatics Research Platform is part of the [Max Planck Unit for the Science of Pathogens (MPUSP)](https://www.mpusp.mpg.de/).
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Our platform specializes in handling and analysis of data from microbial genomics, transcriptomics and other high-throughput experiments.
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Our main objective is the support of all members of MPUSP as well as our collaborators with state-of-the-art tools, workflows, and custom data analyses.
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## Members
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- For a list of actively contributing members, visit our [front page](/).
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- All platform members specialize in different fields of computational microbiology, data science, structural biology, and other topics, creating a diverse skill set
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- Former members of the platform (in chronological order): Dr Davide Chiarugi, Dr Eric Galvez, Timothy Sullivan, and Tristan Kast.
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## Core competences
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- Data acquisition: study and experimental design, analysis strategies, data transfer and stoprage
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- Data quality control (QC): We provide our institute and the public with a range of [data processing workflows](/workflows), all of them featuring rich quality control (QC) functions
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- Data analysis and visualization: One of our most important tasks; We handle primarily short and long read sequencing data for all types of genomics and transcriptomics. Many of our workflows are specially suited for microbial data.
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- Data deposition and sharing: We strive to annotate and deposit all relevant biological data on free and publicly accessible platforms, such as ENA (European Nucleotide Archive) or NCBI's SRA service.
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- Reproducible workflows: We deposit all of our scientific tools and workflows on (https://github.com/MPUSP), with a growing number of repositories being public and freely available under the MIT license.
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## Resources
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We maintain hardware and computational resources that are made available to members and collaborators.
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- In-house CPU-based compute and file servers for data processing
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- GPU servers provided by the [GWDG](https://gwdg.de/en/hpc/systems/scc/), a partner institution of the Max-Planck-Society
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- Possiblity of Oxford-Nanopore sequencing on an in-house instrument
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:::tip Get in touch!
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If your interested in our work, or our portfolio of services, don't hesitate to get in touch. Write to [email protected]
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‎docs/projects/projects/SpyViewer.md‎

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---
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sidebar_position: 1
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---
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# SpyViewer

‎docs/projects/projects/_category_.json‎

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{
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"label": "Projects",
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"position": 2,
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"link": {
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"type": "generated-index",
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"description": "Overview of various projects and workflows for reproducible science."
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}
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}

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