FET: Small: Accurate and Scalable Methods for Analysis of Complex Genomic Populations

University Of Texas At Austin, Austin TX

Investigators

Abstract

Genetic material in living cells and viruses experiences mutations which lead to emergence of diverse genomic populations. In humans, whether inherited or acquired over a lifetime of an individual, genetic mutations impact the individual’s health by causing genetic disorders and rendering the individual predisposed to complex diseases. The onset and progression of cancer, for example, is in part driven by somatic mutations that accumulate over time, creating one or more populations of tumor cells. Genetic variations also occur in viruses where they lead to emergence of rich populations whose spectrum is reflective of the proliferative advantage that particular mutations provide to strains present in the population. Therefore, inferring the composition and studying evolution of genomic populations reveal valuable information about genetic signatures of diseases and generally suggest directions for medical and pharmaceutical research. This research effort will benefit the field and society more broadly through coordinated efforts in innovating in education, enhancing diversity, engaging the community, and disseminating results to a wider public. The aims of this project lie along three integrated research directions: First, one direction is enabling accurate and scalable analysis of diverse mixtures of genomic sequences characterized by accumulated point mutations and insertions/deletions. As part of this research thrust, investigators will introduce deep learning and community detection paradigms to the problems of haplotype assembly and reconstructing viral populations, relying on domain knowledge to inform the design of novel algorithms in those settings. Second, another direction is designing algorithmic frameworks for the reconstruction of the mixtures of sequences characterized by structural variations, such as the variations of copy numbers. Drawing upon ideas from stochastic geometry and mixed integer optimization, investigators will provide novel methods for accurate discovery of the composition of such mixtures, with a focus on mapping intra-tumor heterogeneous genomic landscapes in cancer cells. Third, the final direction involves development of methods for studying dynamics of genomic mixtures that the first two thrusts explore in static settings. In particular, the last research thrust aims to enable tracking the evolution of viral populations in infection networks and the discovery of ancestral relationships between somatic mutations in clonal components of tumor cells. 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.