MINERVA
UX RESEARCH | UX DESIGN | UI DESIGN
MINERVA is a groundbreaking tool for genomic analysis, developed in collaboration with Mayo Clinic’s Center for Individualized Medicine to enhance genetic research and discovery. Leveraging Mayo’s extensive clinical database, MINERVA builds on existing public genetic databases by integrating genotypic data with phenotypic (diagnostic) data, enabling genetic researchers and counselors to uncover more meaningful interpretations of genomic data for individualized medicine. I contributed to MINERVA’s design, leading user research, usability testing, and co-design sessions, as well as serving as the primary production designer for the MVP solution.
RESEARCHSupporting new data for enhanced genetic discovery
The Genome Aggregation Database (gnomAD) is an industry-standard tool used by genetic researchers and counselors for accessing and analyzing genomic data. For example, a genetic counselor may have a patient with a particular variant in their genome. By referencing gnomAD, they can learn about the variant’s prevalence and what gene may be impacted as a result. Despite its wealth of genetic information, however, gnomAD lacks diagnostic information: for example, do patients with a particular variant tend to be diagnosed with a particular disease? MINERVA is designed to bridge this gap. Early contextual inquiry and user research sessions using gnomAD enabled us to develop a preliminary list of requirements and features for MINERVA, understand user flows, and uncover opportunities for integrating Mayo’s clinical diagnostic data to support enhanced discovery.
Early user flows and requirements based on contextual inquiry and user research sessions.
USER INSIGHTCreating a simple system for complex data
User research revealed that geneticists view genetic data at two interconnected levels: variants and genes. Variants, differences in DNA sequences between individuals, live within genes, which are DNA segments often associated with particular functions. Users initially focused on a variant often are interested in understanding the broader context of the associated gene and its role in the human genome, while those examining a gene often need to drill down to specific variants in order to gain more meaningful clinical insights. We centered our application around two core verticals – variant and gene pages – supporting navigation in between and enabling users to explore and interpret complex genetic data in a flow that aligned with existing mental models.
Users can navigate between variant and gene pages, using the main gene page CTA at the top of a variant page, and by drilling into individual variant table rows on a gene page.
CHALLENGEIntegrating existing patterns with new visual cues for more digestible data
A central challenge was iterating on data representations to incorporate new data and improve hierarchy, while leveraging the expertise that users already had with existing tools such as gnomAD. We chose to preserve many of the familiar data groupings from gnomAD, but prioritized user access to the newly-integrated clinical diagnostic information – MINERVA’s key value proposition. We introduced new visual elements, such as colors, symbols, and patterns, drawing from existing genetic annotation platforms to facilitate more efficient data recognition. Features such as summary statistics and selection-responsive tables allow users to understand key metrics at a glance while supporting dynamic, contextual access to detailed data.
On the variant page, we integrated color-coding and symbols from CAVA and ClinVar (genetic annotation platforms), and added visual indicators for variant prevalence (triangles) and diagnostic code levels (tiered bars), in order to highlight key metrics and insights.
Embedded bar charts allow users to quickly asses the high-level distribution of relevant diagnoses, while selection-responsive tables enable users to contextually access details for relevant codes.
APPROACH
Adapting established visualizations for a novel application
One of the core visualizations in MINERVA is a stacked histogram, which depicts the distribution of variants across the genome segment associated with a specific gene. To create this visualization, we leveraged design and interaction precedence across well-known genomic resources such as gnomAD, CAVA, and ClinVar, along with Mayo’s existing workbench. Pie chart filters provide a statistical summary for each annotation class – categories that describe the potential impact of a variant – and paired with the histogram, allow users to quickly assess variant prevalence and clinical impact, and subset the data accordingly. By adapting established patterns to fit MINERVA’s data, we were able to support novel data exploration through familiar, intuitive interactions.
Stacked histograms enable users to survey the distribution of variants within a gene, with a top-bottom comparison of two common annotation systems (CAVA and ClinVar), which document relationships between variants and human health.
DESIGN OUTCOMESMVP impact for genetic exploration
At the end of our collaboration with Mayo, we delivered the MINERVA minimum viable product (MVP): a standalone genetic analysis application that builds on publicly-available genomic repositories by incorporating clinical diagnostic data. Working within the PrimeNG design system, which is utilized across other Mayo workbench software for genetic analysis, MINERVA incorporates familiar interactions and patterns to create an approachable experience for the exploration of novel data.
The variant and gene pages make up the core verticals of MINERVA, organizing genomic data into searchable pages that are distinct yet interconnected.
MINERVA also includes several supporting pages, such as the data summary and FAQ pages, above.
NEXT STEPS & LEARNINGSThe value of collaboration between tools and people
A key next step for MINERVA is enhancing interoperability with other internal tools. Over the course of several years of partnership, our team has helped to create a workbench of software for genetic analysis. While MINERVA is currently a standalone tool, the ability to save, export, and import queries to or from other tools would greatly increase workflow efficiency.
As my first project working with product owners, managers, and embedded research scientists, MINERVA demonstrated the immense value of collaborating within a cross-functional team. With the support of other roles managing strategy, timelines, and roadmapping, I was able to focus primarily on designing user-centered solutions. The deep domain expertise and strategic insights my collaborators contributed not only ensured that our designs were aligned with user needs but also gave me confidence in the impact of our work. This experience reinforced how critical cross-functional collaboration is to creating thoughtful and effective products.