Jupyter Notebooks extension for showing JBrowse views
Project description
JBrowse Jupyter
JBrowse Jupyter is a python package that let's you create JBrowse 2 views in Jupyter notebooks
Demos
- Basic usage -
- SKBR3 cancer cell line demo -
Tutorials
- Tutorial for setting up JupyterLab locally with jbrowse-jupyter https://github.com/GMOD/jbrowse-jupyter-tutorial/
Documentation
More documentation here https://gmod.github.io/jbrowse-jupyter/docs/html/index.html
Installation
$ pip install jbrowse-jupyter
Resources
- JBrowse 2 homepage
- Linear Genome View docs - storybook docs of React LGV
- Dash Applications how to get started to custumize Dash applications.
- Dash HTML components Dash html components to build the Dash application layout.
- Dash JBrowse suite of Dash components for JBrowse views
Contributing
See our contributing guide.
Citation
JBrowse Jupyter: a Python interface to JBrowse 2, Bioinformatics (2023) https://doi.org/10.1093/bioinformatics/btad032
Contact us
- Report a bug or request a feature at https://github.com/GMOD/jbrowse-jupyter/issues
- Join our developers chat at https://gitter.im/GMOD/jbrowse2
- Send an email to our mailing list at
gmod-ajax@lists.sourceforge.net
FAQ
What file types are supported?
We currently support:
- bam/cram
- bigwig
- bigbed
- indexed fasta/bgzip indexed fasta
- twobit
- gff3 tabix
- vcf/vcf tabix
How do I configure text searching?
In order to configure text searching in your Linear Genome View, you must first create a text index. Follow the steps found here. Then you must create and add a text search adapter to your config.
Can I use local files/my own data?
Yes, there are a couple of ways in which you can configure and use your own data from your local environment in jbrowse views. 1. Make use of the jupyter notebook/lab server. Intended for those running their notebooks with jupyter lab or jupyter notebook. 2. Launch your own http server with CORS which will enable you to use local files. You can run our serve.py to launch our dev server. (Checkout our local_support.ipynb for tutorials on how to use your own data)
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