HiGlass Hi-C / genomic / large data viewer / based on original higlass package by Peter Kerpedjiev
Last updated 5 months ago by aostrovsky01 .
MIT · Repository · Bugs · Original npm · Tarball · package.json
$ cnpm install higlass-it 
SYNC missed versions from official npm registry.

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HiGlass is a web-based viewer for datasets too large to view at once. It features synchronized navigation of multiple views as well as continuous zooming and panning for navigation across genomic loci and resolutions. It supports visual comparison of genomic (e.g., Hi-C, ChIP-seq, or bed annotations) and other data (e.g., geographic maps, gigapixel images, or abstract 1D and 2D sequential data) from different experimental conditions and can be used to efficiently identify salient outcomes of experimental perturbations, generate new hypotheses, and share the results with the community.

A live instance can be found at http://higlass.io. A Docker container is available for running an instance locally, although we recommend using the higlass-manage package to start, stop and configure local instances.

For documentation about how to use and install HiGlass, please visit http://docs.higlass.io.


Kerpedjiev, P., Abdennur, N., Lekschas, F., McCallum, C., Dinkla, K., Strobelt, H., ... & Gehlenborg, N. HiGlass: Web-based Visual Exploration and Analysis of Genome Interaction Maps. Genome Biology (2018): 19:125. https://doi.org/10.1186/s13059-018-1486-1



To run higlass from its source code simply run the following:

npm clean-install
npm run start

This starts a server in development mode at http://localhost:8080/.

Once started, a list of the examples can be found at http://localhost:8080/examples.html. Template viewconfs located at /docs/examples/viewconfs can viewed directly at urls such as http://localhost:8080/apis/svg.html?/viewconfs/overlay-tracks.json.


The tests for the React components and API functions are located in the test directory. To save time and only run relevant tests, open karma.conf.js and select the test files to run before running test-watch.

npm run test-watch


  • If the installation fails due to sharp > node-gyp try installing the node packages using python2:

    npm ci --python=/usr/bin/python2 && rm -rf node_modules/node-sass && npm ci


HiGlass provides an API for controlling the component from with JavaScript. Below is a minimal working example to get started and the complete documentation is availabe at docs.higlass.io.

<!DOCTYPE html>
  <meta charset="utf-8">
  <title>Minimal Working Example &middot; HiGlass</title>
  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
  <link rel="stylesheet" href="https://unpkg.com/higlass@1.6.6/dist/hglib.css">

  <style type="text/css">
    html, body {
      width: 100vw;
      height: 100vh;
      overflow: hidden;

  <script crossorigin src="https://unpkg.com/react@16/umd/react.production.min.js"></script>
  <script crossorigin src="https://unpkg.com/react-dom@16/umd/react-dom.production.min.js"></script>
  <script crossorigin src="https://unpkg.com/pixi.js@5/dist/pixi.min.js"></script>
  <script crossorigin src="https://unpkg.com/react-bootstrap@0.32.1/dist/react-bootstrap.min.js"></script>
  <script crossorigin src="https://unpkg.com/higlass@1.6.6/dist/hglib.min.js"></script>
const hgApi = window.hglib.viewer(
  { bounded: true },


diagram of related tools

  • HiGlass Clodius - Package that provides implementations for aggregation and tile generation for many common 1D and 2D data types
  • HiGlass Python - Python bindings to the HiGlass for tile serving, view config generation, and Jupyter Notebook + Lab integration.
  • HiGlass Manage - Easy to use interface for deploying a local HiGlass instance
  • HiGlass Docker - Build an image containing all the components necessary to deploy HiGlass
  • HiGlass Server - Server component for serving multi-resolution data
  • HiGlass App - The code for the web application hosted at http://higlass.io
  • Cooler - Package for efficient storage of and access to sparse 2D data


HiGlass is provided under the MIT License.

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