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SUMMARY:Nguyen\, R. (BMEB) - Development of Computational Methods for Reliable Genetic Identification of Forensic Samples
DESCRIPTION:Advances in sequencing technologies have enabled the recovery of genetic data from minimal\, contaminated\, and highly degraded samples\, overcoming long-standing barriers in forensic analysis. Nevertheless\, many evidentiary samples still yield poor-quality DNA that is unconducive to PCR amplification of short tandem repeats (STRs)\, microarray genotyping\, or deep sequencing necessary for accurate\, complete genotype calls. \nThis dissertation addresses these challenges through the development of computational methods for reliable identity analysis of forensic samples. First\, I present IBDGem\, a fast and robust computational procedure for detecting identity-by-descent (IBD) regions by comparing low-coverage sequence data from an unknown sample against SNP genotype calls from a known individual. Using data from the 1000 Genomes Project and a panel of 8 rootless hairs\, I demonstrate that IBDGem can detect relatedness segments at 1x coverage and achieve high-confidence identifications with as little as 0.01x coverage. \nThe next part of my thesis examines the characteristics of DNA derived from single\, rootless hairs and evaluates their potential as a source of forensic genetic information. Analyses of 80 rootless hair samples reveal DNA fragmentation patterns associated with endonuclease-mediated degradation and nucleosome positioning. This chapter also shows that even short segments of rootless hair shafts can yield adequate sequence data to generate statistical support for or against identity. \nFinally\, I present a comprehensive analysis of IBDGem’s performance across a range of data conditions and program settings. I find that IBDGem is robust to moderate input errors and can identify the major contributor in two-person mixtures. The method also reliably distinguishes self-comparisons from close-relative comparisons\, and remains effective even when limited to 94 target SNPs in the ForenSeq assay. Overall\, these findings establish IBDGem as a practical tool for analyzing trace DNA evidence when conventional methods are unsuccessful. \nEvent Host: Remy Nguyen\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Ed Green \n  \nZoom- https://ucsc.zoom.us/j/91522009894?pwd=JWPSUcIi7IaZ4YOeLDQJohyRApos4T.1 \nPasscode- 854645
URL:https://events.ucsc.edu/event/nguyen-r-bmeb-development-of-computational-methods-for-reliable-genetic-identification-of-forensic-samples/
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CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251113T100000
DTEND;TZID=America/Los_Angeles:20251113T120000
DTSTAMP:20260417T205858
CREATED:20251110T222658Z
LAST-MODIFIED:20251110T222748Z
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SUMMARY:Petety\, A. (CSE) -  New Algorithmic Methods for Uncertain Inputs
DESCRIPTION:This dissertation focuses on designing and proving performance guarantees on algorithms when there is uncertainty in the input. The uncertainty could be from the user being unsure or future inputs that have not arrived yet. We look at different methods in which algorithms can be designed to be competitive against the optimal. One of the assumptions that helps in this is to assume that the input arrival order is completely random. We study the online load/graph balancing problem when the input arrival order is uniformly random. We show lower bounds for the greedy algorithm and the general case. In the next part\, we study the online scheduling problem under the assumption that the online algorithm has an additional ϵ speed compared to the machines in offline optimal. We show a meta algorithm generalizing Shortest Remaining Processing Time that gives a scalable algorithm for minimizing total weighted flow time. We show that it achieves scalability for minimizing total weighted flow time when the residual optimum exhibits supermodularity. In the final part we look at the online caching problem when the algorithm has access to ML-augmented predictions. We propose an algorithm that achieves a O(logb k) competitive ratio even when using just b predictions per cache miss. We also prove its robustness and consistency. \nEvent Host: Aditya Petety\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Sungjin Im \n 
URL:https://events.ucsc.edu/event/petety-a-cse-new-algorithmic-methods-for-uncertain-inputs/
LOCATION:CA
CATEGORIES:Ph.D. Presentations
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