Probably Genetic used Urchin to answer a critical strategic question: how could they maximize the value of their genomic data asset for drug development?

Context

Probably Genetic is an AI x genomics platform company. Its core asset is a proprietary dataset of patients with a range of rare and common diseases comprising genetic sequencing results linked to self-reported symptoms, multi-modal phenotypic data (e.g., videos, images) and clinical outcomes (e.g., EHR).

The strategic question facing the company was how to maximize the value of its growing data asset: whether to prioritize scale or depth, and which indications to focus on. Each strategy would require investment in different core activities and capabilities, affecting both hiring plans and how they positioned the company to both investors and potential partners.

The Challenge

Whether cohort size or data depth matters more depends on the disease area, the underlying biology, and the downstream use case. Rare variant discovery rewards larger cohorts, while target validation, patient stratification, biomarker discovery, and trial design reward richer per-patient data. Probably Genetic's cohort sat squarely in the middle: large enough that either direction was defensible, in a range of disease areas where both breadth and depth have legitimate scientific and commercial buyers.

Making an informed decision therefore required synthesizing evidence that was scattered across many domains: disease biology, patient stratification, biopharma partnership strategy, market comparables, and the operational realities of different growth paths. The challenge was assembling enough context from across these domains to understand the tradeoffs and make a well-informed strategic choice.

What Urchin Did

Urchin conducted a comprehensive analysis of the biopharma target validation landscape using Probably Genetic's specific asset profile as the lens.

The engagement:

• Mapped the biological use cases for genomic data partnerships — identifying which scientific questions benefit primarily from cohort scale (such as rare variant discovery and genetic association studies) and which depend on deeper molecular and clinical characterization (including target validation, patient stratification, biomarker discovery, and translational research).
• Analyzed partner demand and value creation — evaluating which biopharma partners were actively seeking these different classes of evidence and how dataset characteristics influenced partnership economics and deal structure.
• Benchmarked competitive datasets and market comparables — identifying where the company's existing assets were differentiated and where additional investment would create the greatest strategic advantage.
• Modeled the operational implications of alternative growth paths — evaluating the organizational capabilities, analytical infrastructure, and investment required to realize value from each strategy.

The market comparables provided an important additional lens for the executive team. While the biological analysis clarified where breadth and depth create scientific value, recent biopharma partnership deals revealed an order-of-magnitude difference in how genomic data assets are valued commercially. Quantifying that gap helped the team connect scientific strategy with capital allocation and company building.

Impact

The analysis informed how Probably Genetic thought about building the company. The work clarified the distinct biological and commercial opportunities unlocked by each path. This gave the team a framework for reasoning explicitly about where scale creates value, where depth creates value, and how those tradeoffs should shape capital allocation, hiring, and partnership strategy.