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DTSTART;TZID=America/Los_Angeles:20251110T130000
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DTSTAMP:20251028T155148Z
CREATED:20251028T155007Z
LAST-MODIFIED:20251028T155148Z
UID:10005010-1762779600-1762786800@events.ucsc.edu
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/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20251110T160000
DTEND;TZID=America/Los_Angeles:20251110T170000
DTSTAMP:20251022T182510Z
CREATED:20251003T195525Z
LAST-MODIFIED:20251022T182510Z
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SUMMARY:AM Seminar: Structure-Preserving Discretizations and their Applications
DESCRIPTION:Presenter: Andy Wan\, Assistant Professor\, University of California\, Merced \n  \nDescription: Many models from science and engineering possess fundamental structures which are important to preserve in order for accurate and stable long-term predictions. For instance\, preserving conserved quantities\, such as energy\, mass and momentum\, are fundamental in many physical systems. Moreover\, preserving dissipative quantities\, such as entropy or Lyapunov functions\, are also essential for predicting correct asymptotic limits. In this talk\, we will survey a recent new class of conservative and dissipation-preserving integrators\, called the Discrete Multiplier Method (DMM). We will discuss various applications to many-body systems\, geodesic flow\, and particle methods in fluids and kinetic models. Moreover\, we will introduce Conservative Hamiltonian Monte Carlo\, which utilizes DMM to improve sampling efficacy of Hamiltonian Monte Carlo for high dimensional target distributions. If time permits\, we will also discuss how structure-preservation in scientific machine learning can improve long-term predictions and be amenable to error analysis on accuracy bounds. \n  \nBio: Andy Wan is an Assistant Professor in the Department of Applied Mathematics at the University of California\, Merced (UC Merced). Prior to joining UC Merced in 2024\, he received his Ph.D. from Polytechnique Montreal\, and was a postdoctoral fellow at McGill University and later an assistant professor at the University of Northern British Columbia. His research interests are in numerical analysis\, scientific computing\, and scientific machine learning. He focuses on structure-preserving discretizations\, specifically in the theory and development of conservative and dissipation-preserving integrators\, as well as their applications to mathematical sciences\, computational statistics and scientific machine learning. He is currently a co-investigator of the 2024-2027 Collaborative Research Group on “Structure-Preserving Discretizations and their Applications”\, supported by the Pacific Institute for the Mathematical Sciences (PIMS). He has also recently co-organized a summer school and hackathon event on “Structure-Preserving Scientific Computing and Machine Learning”\, supported by NSF and PIMS. \n  \nHosted by: Professor Julie Simons \n 
URL:https://events.ucsc.edu/event/am-seminar-structure-preserving-discretizations-and-their-applications/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
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