https://github.com/bojarlab/canonicalize_app
Canonicalize glycan sequences via a web app
Science Score: 26.0%
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
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.6%) to scientific vocabulary
Keywords
Repository
Canonicalize glycan sequences via a web app
Basic Info
- Host: GitHub
- Owner: BojarLab
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://canonicalize.streamlit.app/
- Size: 8.79 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
IUPAC Glycan Sequence Canonicalizer
A simple web application for canonicalizing IUPAC glycan sequences using the glycowork Python package.
About
This tool provides a convenient interface to convert glycan sequences into their canonicalized IUPAC representation. Simply paste your sequences, click convert, and get standardized results instantly.
Features
- Easy-to-use web interface
- Batch processing of multiple sequences
- Instant conversion using the
canonicalize_iupacfunction from glycowork - Error handling for invalid sequences
Usage
- Enter one or more glycan sequences in the input text area (one per line)
- Click the "Convert" button
- View and copy the canonicalized sequences from the output area
Local Development
To run this application locally:
pip install -r requirements.txt
streamlit run app.py
Deployment
This application is deployed on Streamlit Cloud and is freely accessible at the canonicalize app.
Dependencies
- streamlit
- glycowork
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contact
For questions or issues related to this application, please open an issue on this repository. For questions about the glycowork package, visit the glycowork repository.
Owner
- Name: BojarLab
- Login: BojarLab
- Kind: organization
- Email: daniel.bojar@gu.se
- Location: Gothenburg, Sweden
- Website: https://dbojar.com/bojar-lab/
- Twitter: daniel_bojar
- Repositories: 4
- Profile: https://github.com/BojarLab
Machine Learning in Glycobiology and Systems Biology
GitHub Events
Total
- Push event: 5
- Create event: 2
Last Year
- Push event: 5
- Create event: 2