https://github.com/cbg-ethz/v-pipe-scout

Rapid Viral Variant Exploration and Detection - Interactive WebApp

https://github.com/cbg-ethz/v-pipe-scout

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
    Links to: biorxiv.org
  • Committers with academic emails
    1 of 4 committers (25.0%) from academic institutions
  • Institutional organization owner
    Organization cbg-ethz has institutional domain (www.bsse.ethz.ch)
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.0%) to scientific vocabulary

Keywords

biohackeu24 elixir-cloud-aai

Keywords from Contributors

archival projection interactive generic sequences ecosystem-modeling autograding hacking shellcodes modular
Last synced: 6 months ago · JSON representation

Repository

Rapid Viral Variant Exploration and Detection - Interactive WebApp

Basic Info
Statistics
  • Stars: 0
  • Watchers: 3
  • Forks: 1
  • Open Issues: 9
  • Releases: 4
Topics
biohackeu24 elixir-cloud-aai
Created over 1 year ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

V-Pipe Scout: Rapid Interactive Viral Variant Detection

POC Python Streamlit License: MIT

Overview

Recognizing and quantifying viral variants from wastewater requires expert human judgment in the final steps. V-Pipe Scout allows for rapid exploration of wastewater viral sequences down to the single read level.

Its aim: Discover novel viral threats a few weeks earlier than traditional methods.

This Proof-of-Concept is set up for SARS-CoV-2, yet is built to be virus-agnostic and will be expanded to RSV and Influenza soon.

This is an effort of the V-Pipe team. For more information about V-Pipe, visit the V-Pipe website.

Fast Query Visualization

Technical architecture for real-time visualization of viral sequencing data & rapid on-demand analysis

Specifically, V-Pipe Scout enables: - Exploration of mutations at the read level
- For known resistance mutations
- Guided by smart filters and variant signatures - Composition of variant signatures for abundance estimates
- Leveraging clinical sequence databases (e.g., CovSpectrum)
- Using curated variant signatures - URL-based session sharing
- Share analysis configurations via URLs - Collaborate by sharing specific page setups - Bookmark and resume analysis sessions

Further, we will implement: - On-demand variant abundance estimates by Lollipop

V-Pipe Scout brings together: - V-pipe - our prime Wastewater Viral Analysis Pipeline, see publication. - GenSpectrum - in particular the novel fast database for genomic sequences LAPIS-SILO, see publication

This application relies on two other repos as connecting infrastructure: - WisePulse - to pre-process and run the SILO database, powering read-level queries - sr2silo - large scale data-wrangler of nucleotide alignments, to amino-acids and SILO input format

Deployment

The current deployment of this project can be accessed at dev.vpipe.ethz.ch. Only accessible within ETH Zürich Networks.

Installation

  1. Clone the repository: sh git clone https://github.com/cbg-ethz/v-pipe-scout.git cd v-pipe-scout

  2. Setup environment: sh ./setup.sh # Creates .env with secure Redis password (single source of truth)

  3. Configure LAPIS connection in app/config.yaml: yaml server: lapis_address: "http://host.docker.internal:8083" # For local LAPIS

  4. Run the application: sh docker compose up --build

Automatic Deployment

For production deployments on VMs or servers, you can set up automatic deployment to eliminate the need for manual updates. See DEPLOYMENT.md for detailed instructions on:

  • Setting up automatic deployment with cron jobs
  • Monitoring and logging deployment activities
  • Configuring rollback mechanisms
  • Troubleshooting deployment issues

Architecture

  • Streamlit Frontend: Interactive web interface
  • Celery Worker: Background task processing
  • Redis: Message broker (password-protected, internal only)

Project Origin

This project was initiated as part of a hackathon project at the BioHackathon Europe 2024.

CI/CD

Testing: Automated tests for frontend, worker, and full Docker Compose stack run on every push/PR.

Deployment: - Development: auto-deploy.sh runs every 5 minutes via cron → dev.vpipe.ethz.ch (ETH network only) - Production: deploy.yml triggers on GitHub releases for production deployment

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Owner

  • Name: Computational Biology Group (CBG)
  • Login: cbg-ethz
  • Kind: organization
  • Location: Basel, Switzerland

Beerenwinkel Lab at ETH Zurich

GitHub Events

Total
  • Create event: 44
  • Release event: 3
  • Issues event: 64
  • Delete event: 33
  • Member event: 2
  • Issue comment event: 34
  • Push event: 181
  • Pull request review comment event: 86
  • Pull request review event: 77
  • Pull request event: 78
Last Year
  • Create event: 44
  • Release event: 3
  • Issues event: 64
  • Delete event: 33
  • Member event: 2
  • Issue comment event: 34
  • Push event: 181
  • Pull request review comment event: 86
  • Pull request review event: 77
  • Pull request event: 78

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 343
  • Total Committers: 4
  • Avg Commits per committer: 85.75
  • Development Distribution Score (DDS): 0.257
Past Year
  • Commits: 343
  • Committers: 4
  • Avg Commits per committer: 85.75
  • Development Distribution Score (DDS): 0.257
Top Committers
Name Email Commits
Gordon J. Köhn g****n@d****h 255
Gordon J. Köhn g****n@k****t 65
copilot-swe-agent[bot] 1****t 22
dependabot[bot] 4****] 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 49
  • Total pull requests: 87
  • Average time to close issues: 6 days
  • Average time to close pull requests: about 15 hours
  • Total issue authors: 1
  • Total pull request authors: 3
  • Average comments per issue: 0.41
  • Average comments per pull request: 0.33
  • Merged pull requests: 65
  • Bot issues: 0
  • Bot pull requests: 2
Past Year
  • Issues: 49
  • Pull requests: 87
  • Average time to close issues: 6 days
  • Average time to close pull requests: about 15 hours
  • Issue authors: 1
  • Pull request authors: 3
  • Average comments per issue: 0.41
  • Average comments per pull request: 0.33
  • Merged pull requests: 65
  • Bot issues: 0
  • Bot pull requests: 2
Top Authors
Issue Authors
  • gordonkoehn (49)
Pull Request Authors
  • gordonkoehn (77)
  • Copilot (8)
  • dependabot[bot] (2)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels
enhancement (8) dependencies (2) github_actions (2) bug (2) documentation (1)