Short Tandem Repeat Instability QuantIfier for Next-Generation sequencing

Objetivo

Over 50 severe genetic disorders are caused by short tandem repeat (STR) expansions. Emerging evidence indicates that increased STR instability plays a critical role in their pathogenesis. However, current analysis tools can only determine basic STR length but not instability, severely limiting both therapeutic development and clinical care. Our project aims to commercialize innovative software that enables targeted and genome-wide quantification of STR instability, addressing urgent needs in drug development, clinical diagnostics, and research. Building on successful prototype development through our ERC Starting Grant TRANSIT-ND, we will transform our computational methods into a validated commercial product. Our solution combines novel filter-based algorithms with machine learning approaches to achieve superior accuracy and speed in STR analysis. Uniquely, it can quantify somatic instability - a crucial parameter for therapeutic development beyond the reach of existing tools. The project comprises comprehensive technical validation using large cohorts, matched tissue samples, and clinical cases. In parallel, we will develop market entry strategies, secure IP protection, and establish regulatory compliance pathways. Our dual-licensing model will serve both commercial users (pharmaceutical companies, clinical laboratories) and academic researchers. The market opportunity is substantial, driven by increasing investment in STR-targeting therapies and growing demand for comprehensive genetic testing. Our software will enable pharmaceutical companies to better evaluate therapeutic efficacy, help clinical laboratories identify at-risk individuals earlier, and accelerate research into novel pathogenic variants. The project will advance our technology from TRL 2 to TRL 4, positioning us for follow-on funding through EIC Transition. Success will substantially impact both therapeutic development for currently untreatable disorders and early detection of individuals at risk.