Algorithms for phylogenetic tree correction in species and cancer evolution

Reconstructing evolutionary trees, also known as phylogenies, from molecular sequence data is a fundamental problem in computational biology. Classically, evolutionary trees have been estimated over a set of species, where leaves correspond to extant species and internal nodes correspond to ancestral species. This type of phylogeny is colloquially thought of as the “Tree of Life” and assembling it has been designated as a Grand Challenge by the National Science Foundation Advisory Committee for Cyberinfrastructure. However, processes other than speciation are also shaped by evolution. One notable example is in the development of a malignant tumor; tumor cells rapidly grow and divide, acquiring new mutations with each subsequent generation. Tumor cells then compete for resources, often resulting in selection for more aggressive cell types. Recent advancements in sequencing technology rapidly increased the amount of sequencing data taken from tumor biopsies. This development has allowed researchers to attempt reconstructing evolutionary histories for individual patient tumors, improving our understanding of cancer and laying the groundwork for precision therapy. Despite algorithmic improvements in the estimation of both species and tumor phylogenies from molecular sequence data, current approaches still suffer a number of limitations. Incomplete sampling and estimation error can lead to missing leaves and low-support branches in the estimated phylogenies. Moreover, commonly posed optimization problems are often under-determined given the limited amounts and low quality of input data, leading to large solution spaces of equally plausible phylogenies. In this dissertation, we explore current limitations in both species and tumor phylogeny estimation, connecting similarities and highlighting key differences. We then put forward four new methods that improve phylogeny estimation methods by incorporating auxiliary information: OCTAL, TRACTION, PhySigs, and RECAP. For each method, we present theoretical results (e.g., optimization problem complexity, algorithmic correctness, running time analysis) as well as empirical results on simulated and real datasets. Collectively, these methods show we can significantly improve the accuracy of leading phylogeny estimation methods by leveraging additional signal in distinct, but related datasets

Lincoln Upper Elementary School Washington, Iowa I-WALK Report Spring 2012

In the past three decades, the number of obese and overweight individuals in Iowa and across the nation has skyrocketed. With obesity comes the greater risk of health complications and life expectancy reduction. As a result, the current generation of youth face a new and growing threat to their overall quality of life. In Iowa alone, 37.1% of 3rd grade students are identified as either overweight or obese.https://lib.dr.iastate.edu/iwalk_reports/1031/thumbnail.jp

DU Undergraduate Showcase: Research, Scholarship, and Creative Works

DU Undergraduate Showcase: Research, Scholarship, and Creative Work

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine