Multi-Focus Querying of the Human Genome Information on Desktop and in Virtual Reality: an Evaluation

TL;DR

This study evaluates virtual reality for human genome visualization, finding VR preferred by users and that gene information placement affects task performance, with embedded info aiding identification and separated info supporting comparisons.

Contribution

It introduces a VR-based genome visualization approach and assesses optimal gene information placement through user studies, comparing VR and desktop methods.

Findings

Users prefer VR despite longer task times.

Embedded gene info improves target identification and summarization.

Separated gene info enhances region comparison.

Abstract

The human genome is incredibly information-rich, consisting of approximately 25,000 protein-coding genes spread out over 3.2 billion nucleotide base pairs contained within 24 unique chromosomes. The genome is important in maintaining spatial context, which assists in understanding gene interactions and relationships. However, existing methods of genome visualization that utilize spatial awareness are inefficient and prone to limitations in presenting gene information and spatial context. This study proposed an innovative approach to genome visualization and exploration utilizing virtual reality. To determine the optimal placement of gene information and evaluate its essentiality in a VR environment, we implemented and conducted a user study with three different interaction methods. Two interaction methods were developed in virtual reality to determine if gene information is better suited to be embedded within the chromosome ideogram or separate from the ideogram. The final ideogram interaction method was performed on a desktop and served as a benchmark to evaluate the potential benefits associated with the use of VR. Our study findings reveal a preference for VR, despite longer task completion times. In addition, the placement of gene information within the visualization had a notable impact on the ability of a user to complete tasks. Specifically, gene information embedded within the chromosome ideogram was better suited for single target identification and summarization tasks, while separating gene information from the ideogram better supported region comparison tasks.