igv-jupyterlab 1.0.1

igv-jupyterlab is an extension for Jupyter Lab and traditional Jupyter Notebooks which wraps igv.js.

igv-jupyterlab

igv-jupyterlab is an extension for Jupyter Lab and traditional Jupyter Notebooks which wraps igv.js.

Installation

You can install using pip:

pip install igv_jupyterlab

If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable the nbextension:

jupyter nbextension enable --py [--sys-prefix|--user|--system] igv_jupyterlab

Usage

This extension provides a python wrapper which allows you render igv.js in a cell and call its API from the notebook.

Initialization

To insert an IGV instance into a cell:

from igv_jupyterlab import IGV

# At minimum, IGV requires a single argument, genome.

# For supported genomes, a simple name may be supplied.
IGV(genome="hg19")

# For all other genomes, we must construct a configuration object.
# A helper method supplied to make this easier.
genome = IGV.create_genome(
    name="Human (GRCh38/hg38)",
    fasta_url="https://s3.amazonaws.com/igv.broadinstitute.org/genomes/seq/hg38/hg38.fa",
    index_url="https://s3.amazonaws.com/igv.broadinstitute.org/genomes/seq/hg38/hg38.fa.fai",
    cytoband_url="https://s3.amazonaws.com/igv.broadinstitute.org/annotations/hg38/cytoBandIdeo.txt",
)

igv = IGV(genome=genome)

display(igv)

Supported genomes are listed here. Reference configuration is described in the igv.js documentation.

# It is also easy to change the genome to something else
some_other_genome = igv.create_genome(...)

igv.load_genome(some_other_genome)

Tracks

To load a track pass a track configuration object to load_track(). Track configuration objects are described in the igv.js documentation.

Remote URL

track = IGV.create_track(
    name="Segmented CN",
    url="https://data.broadinstitute.org/igvdata/test/igv-web/segmented_data_080520.seg.gz",
    format="seg",
    indexed=False
)

igv.load_track(track)

Local File

Tracks can be loaded from local files using the Jupyter web server by prepending "tree" to the path.

track = IGV.create_track(
    name="Local VCF",
    url="/tree/absolute/path/to/example.vcf",
    format="vcf",
    type="variant",
    indexed=False
)

igv.load_track(track)

Remove a track

# It is easy to remove a track by name
igv.remove_track("HG00103")

Navigation

Zoom in by a factor of 2

igv.zoom_in()

Zoom out by a factor of 2

igv.zoom_out()

Jump to a specific locus

igv.locus = 'chr1:3000-4000'

# A helper method is available to avoid having to perform string formatting
igv.search_locus('chr1', 3000, 4000)

Jump to a specific gene. This uses the IGV search web service, which currently supports a limited number of genomes: hg38, hg19, and mm10. To configure a custom search service see the igv.js documentation

igv.locus = 'myc'

SVG output

Displaying the current IGV view as an SVG is simple - and only requires one call now!

igv.get_svg()

Development Installation

Create a dev environment:

conda create -n igv_jupyterlab-dev -c conda-forge nodejs yarn python jupyterlab
conda activate igv_jupyterlab-dev

Install the python. This will also build the TS package.

pip install -e ".[test, examples]"

When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For lab, this is done by the command:

jupyter labextension develop --overwrite .
yarn run build

For classic notebook, you need to run:

jupyter nbextension install --sys-prefix --symlink --overwrite --py igv_jupyterlab
jupyter nbextension enable --sys-prefix --py igv_jupyterlab

Note that the --symlink flag doesn't work on Windows, so you will here have to run the install command every time that you rebuild your extension. For certain installations you might also need another flag instead of --sys-prefix, but we won't cover the meaning of those flags here.

How to see your changes

Typescript:

If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the widget.

# Watch the source directory in one terminal, automatically rebuilding when needed
yarn run watch
# Run JupyterLab in another terminal
jupyter lab

After a change wait for the build to finish and then refresh your browser and the changes should take effect.

Python:

If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.