Abstract
We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.
Highlights
The development of chromosome capture assays measuring the spatial contacts between two or more regions of the genome is essential for elucidating how the structure and dynamics of the genome affect gene regulation and cellular function [1, 2]
We added views of genomic positions and locations of individual genes that move in sync with Hi-C maps, allowing examination of changes in Hi-C data in different genic contexts
We identify the contact patterns that disappeared as topologically associating domains (TADs) in a strict sense because they do not show the long-range associative “checkerboard” pattern of A/B compartmentalization, a feature that remains intact in the ΔNipbl condition [34]
Summary
The development of chromosome capture assays measuring the spatial contacts between two or more regions of the genome is essential for elucidating how the structure and dynamics of the genome affect gene regulation and cellular function [1, 2]. Despite the large amounts of generated Hi-C data, major challenges remain in (i) identifying known features unambiguously [14]; (ii) discovering new features; (iii) establishing relationships between Hi-C features and known (epi)genetic profiles; (iv) establishing the effects of various genetic, biochemical, and physical perturbations on chromatin organization, assessing meaningful differences between cell types [15], and assessing changes across the cell cycle and along differentiation pathways [16] These challenges necessitate the development of methods to visually explore, compare, and share the raw data and related datasets and derived analysis results. An effective platform does this all in a fast, intuitive, and accessible manner
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