The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the development and validation of a new approach to facilitate population-level access to genetic information contained in the human genome currently inaccessible. Importantly, it can drive a significant reduction in time and cost needed to scan the genome of patients who suffer from a specific disease, from years to months, accelerating discovery cycles and producing data with higher resolution to develop better diagnostic tools or treatments. The proposed project describes a novel approach for high-resolution human genome sequencing using unique computational algorithms that facilitate detection of diverse types of genetic variation using a pangenomics approach, implemented directly on raw sequencing data. Genetic variation inadequately captured by short-read Next Generation Sequencing, such as large chromosomal rearrangements, long tandem repeats, or long-range haplotype configurations, represent a significant gap in knowledge. In the last few years, maturation of long-read Next Generation Sequencing (NGS) technologies have begun uncovering the role of Structural Variants (SVs) and haplotypes in disease. However, the main limitation to expand implementation of long-read NGS is the cost of generating sufficient sequencing coverage to compensate for the relatively high natural error rate. The proposed technology can address this issue directly and facilitate cost-effective long-read sequencing of human genomes at scale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
