A graphics toolkit for visualizing genome data - Nature.com

A graphics toolkit for visualizing genome data - Nature.com

A graphics toolkit for visualizing genome data - Nature.com
Aug 16, 2022 2 mins, 48 secs

The genomics world has no shortage of visualization tools.

Now, a tool known as Gosling allows bioinformaticians to build apps that can display genomic information with the same level of flexibility that developers have come to expect from other graphics programming tools.

First released in 2020 by bioinformatician Nils Gehlenborg and his team at Harvard Medical School in Boston, Massachusetts, Gosling stands for ‘grammar of scalable linked interactive nucleotide graphics’1.

Researchers and bioinformaticians can use these libraries to create interactive, scalable visualizations that they can share with their colleagues, and to build bespoke genetic-analysis tools.

An online testing environment provides visualizations that users can extend to create and export their own graphics.

But these tools can be used for any type of graphic, whereas Gosling is specifically designed for genomics visualizations.

Programming tools for visualizations range from template-based functions that use a single line of code to create a standard type of graph to those that assemble visualizations piece by piece from lines and geometric shapes, such as the JavaScript D3.js library.

“Gosling really bridges that gap, making it way easier to make new tools that have visualization components,” says Maria Nattestad, a software engineer at Google in Mountain View, California.

Gehlenborg says that Gosling arose out of a 2019 literature review2, during which his team surveyed the genome visualization landscape and built up a taxonomy for the tools and their capabilities.

Gosling, Gehlenborg explains, “is a fundamental approach to assemble genomic visualizations using that same taxonomy”.

The final visualization is drawn in a web browser using a rendering engine and file-formatting tools developed by the Gehlenborg team to visualize chromosomal data from a technique called Hi-C3.

Postdoc Sehi L’Yi, who led Gosling’s development, says that what differentiates Gosling from other visualization tools is its expressiveness.

“It is really not easy to customize visualizations as a user.” But with Gosling, users can, for instance, specify the colour, dimensions and placement of the symbol used to represent a centromere or genomic interval, then overlay that on an ideogram of a chromosome to highlight a region of interest.

A team of master’s students at the University of British Columbia decided to use Gosling to create its final project in a data-visualization class.

That said, she notes that they initially struggled to extract the data they needed to allow users to click on regions and create new visualizations.

Dominic Girardi, chief product officer at the data visualization company Datavisyn in Linz, Austria, has also experimented with Gosling to create an interactive playground that allows users to filter a table of genes by genomic region.

The firm — which Gehlenborg co-founded — is now using Gosling to generate visualization tools for its corporate clients, although it has not yet completed one, Girardi says.

Gosling isn’t the only visualization library for genomic data; other examples include ggbio, gggenomes and gggenes, all of which are extensions of the ggplot2 graphing library.

But most of these tools create static images, Gehlenborg says — pictures, rather than interactive visualizations.

Gehlenborg says that future plans for Gosling include giving it a graphical interface, so that researchers can create visualizations by dragging-and-dropping widgets onto a virtual canvas rather than having to program them.

Robert Buels, who is leading development of a genome browser at the University of California, Berkeley, says that Gosling “occupies a really interesting space” in the genomics visualization toolbox.

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