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DTSTART;TZID=America/Los_Angeles:20260423T114000
DTEND;TZID=America/Los_Angeles:20260423T131500
DTSTAMP:20260502T012304
CREATED:20260423T150019Z
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SUMMARY:BME 280B Seminar: Speaker Dr. Aaron Newman - Molecular and spatial determinants of single-cell developmental states in cancer
DESCRIPTION:Presenter: Dr. Newman\, Associate Professor in the Department of Biomedical Data Science\, Stanford University \n  \nDescription: Determining the factors that shape cell potency—the ability of a cell to differentiate into other cell types—is essential for understanding tissue biology in health and disease\, including cancer. In previous work\, we found that single-cell transcriptional diversity decreases across developmental time\, from the fertilized egg to the most mature cells in the body\, and in multiple organisms. More recently\, we developed CytoTRACE 2\, an interpretable AI framework trained on millions of data points from single-cell RNA sequencing data\, to determine cell potency on an absolute scale and reveal molecular hallmarks of developmental potential. We are now leveraging this method along with advances in spatial transcriptomics\, to identify multicellular ecosystems linked to cancer cell differentiation states and clinical outcomes. I will highlight these tools along with our ongoing work to decode cell plasticity and clinically relevant spatial microenvironments in human malignancies. \n  \nBio: Dr. Newman is an Associate Professor in the Department of Biomedical Data Science at Stanford University and a Chan Zuckerberg Biohub Investigator. He is also a member of the Stanford Cancer Institute and the Stanford Institute for Stem Cell Biology and Regenerative Medicine. Dr. Newman has made significant contributions to computational biology with applications to liquid biopsy\, cancer genomics\, and tumor immunology. Key contributions include CAPP-Seq for ultrasensitive detection of circulating tumor DNA; CIBERSORT/x for decoding cellular composition from bulk genomic data; CytoTRACE/2 for inferring cellular differentiation states from scRNA-seq data; and EcoTyper for delineating context-dependent cellular ecosystems from bulk\, single-cell\, and spatial expression data. His research program focuses on developing innovative data science tools to study the phenotypic diversity\, differentiation hierarchies\, and clinical significance of tumor cells and their surrounding microenvironments. Key results are further explored experimentally\, both in the lab and through collaboration\, with the goal of translating promising findings into the clinic.  \nHosted by: Professor Camilla Forsberg\, BME Department
URL:https://events.ucsc.edu/event/molecular-and-spatial-determinants-of-single-cell-developmental-states-in-cancer/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260402T114000
DTEND;TZID=America/Los_Angeles:20260402T131500
DTSTAMP:20260502T012304
CREATED:20260401T004313Z
LAST-MODIFIED:20260401T004313Z
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SUMMARY:BME 280B Seminar: Small changes\, Big consequences: Modulators of Alphavirus Assembly
DESCRIPTION:Presenter: Dr. Suchetana (Tuli) Mukhopadhyay\, Professor\, Indiana University \nDescription: N/A \nBio: Suchetana “Tuli” Mukhopadhyay\, Ph.D.\, is a professor in the Department of Biology at Indiana University\, Bloomington. She received her B.A. in chemistry from DePauw University and her Ph.D. in chemistry from the University of Illinois at Chicago. Following her doctoral studies\, Mukhopadhyay conducted postdoctoral research at the University of Texas Southwestern Medical Center\, focusing on G-protein mediated signaling. She continued her postdoctoral work at Purdue University in structural virology\, where she developed a strong interest in arboviruses. Mukhopadhyay joined Indiana University in 2005\, where she established her research program on the assembly and spread of alphaviruses. \nHosted by: Professor Rebecca Dubois\, BME Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-small-changes-big-consequences-modulators-of-alphavirus-assembly/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251211T090000
DTEND;TZID=America/Los_Angeles:20251211T110000
DTSTAMP:20260502T012304
CREATED:20251202T162054Z
LAST-MODIFIED:20251209T161343Z
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SUMMARY:Tran\, L. (BMEB) -  Polysome Shadowing: A Long-Read Sequencing Approach to Study Translation
DESCRIPTION:Translation is a central and highly regulated step of gene expression\, yet there are few quantitative\, high-throughput tools to study translation. Existing methods such as sucrose gradients provide only bulk ribosome counts\, while Ribo-Seq offers positional information in the genome but destroys long-range structure and transcript expression information. Because of these limitations\, many fundamental questions about mRNA translation into protein remain difficult to assay. In this proposal\, I outline my plans to develop a novel technology\, deemed Polysome Shadowing\, that covalently marks ribosome-unprotected regions of RNA with hyperactive base editors. Because ribosomes protect ~21–30 nt regions of mRNAs\, ribosome “shadows” appear as tracts of unedited bases in long-read sequencing. In Aim 1\, I will identify ribosome shadows on single molecules by increasing editing efficiency through optimization of dual cytosine and adenosine base editors and statistical modeling. In Aim 2\, I will maximize the accuracy of information recovered from highly-edited RNAs by developing a multipass library preparation protocol to generate high-confidence reads. In Aim 3\, I will apply the tools I have already developed to examine previously difficult-to-assay paradigms of translational control in the form of viral frameshifting mechanisms. Together\, completion of these aims will build an information-rich sequencing technology capable of positioning ribosomes on intact mRNAs while preserving long-range information and establish feasibility to study nascent paradigms. \nHost: Liam Tran\, Ph.D. Student\, Biomolecular Engineering and Bioinformatics  \nAdvisor: Joshua Arribere 
URL:https://events.ucsc.edu/event/tran-l-bmeb-polysome-shadowing-a-long-read-sequencing-approach-to-study-translation/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250828T110000
DTEND;TZID=America/Los_Angeles:20250828T110000
DTSTAMP:20260502T012304
CREATED:20250821T070000Z
LAST-MODIFIED:20250925T231433Z
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SUMMARY:Shanks\, C. (BMEB) -  Development and Application of Local Ancestry Methods for Population Genomics
DESCRIPTION:Local ancestry methods classify the segments of DNA inherited from a specific ancestry (e.g.\, African\, East Asian\, European)\, improving analyses of admixed populations. Aim 1 applies ancestry-specific analysis to more than 1000 whole genome sequences across Polynesia\, revealing strong bottlenecks in the voyagers who settled both Hawaiʻi and Rapa Nui\, and confirming frequencies of Mendelian disease–causing variants in French Polynesia not found even in large-scale biobanks. Conventional local ancestry methods are only accurate for deeply diverged populations and recent admixture events. In aim 2 I present ARGMix\, a new deep-learning approach incorporating ancestral recombination graphs (ARGs)\, which contains the inferred history of coalescent events with recombination. This method classifies the local ancestry of present day Europeans as originating from early European farmers and hunter gather ancestries. As an application I find evidence of continuity between Ötzi the Iceman and present-day Europeans of similar geography. I further propose to apply ARGMix to trace the TCC/TTC mutational pulse strongest in Europeans across geography and time. In aim 3 I present a method incorporating these ancient local ancestries to improve polygenic risk scores in the UK biobank. \nEvent Host: Cole Shanks\, Ph.D Student\, Biomolecular Engineering & Bioinformatics
URL:https://events.ucsc.edu/event/shanks-c-bmeb-development-and-application-of-local-ancestry-methods-for-population-genomics/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250827T120000
DTEND;TZID=America/Los_Angeles:20250827T120000
DTSTAMP:20260502T012304
CREATED:20250819T070000Z
LAST-MODIFIED:20250925T231630Z
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SUMMARY:Leavitt\, J. (BMEB) - Evolutionary Dynamics\, Functional Adaptations in Stress Response\, and Direct Detection of tRNA modifications in Archaea
DESCRIPTION:Transfer RNA (tRNA) modifications are essential for structural integrity\, decoding fidelity\, and stress adaptation\, yet their dynamics across phylogenetically distinct archaeal species and their functional roles during stress remain incompletely understood. This dissertation aims to address some of these gaps through a multi-scale investigation that spans the evolutionary dynamics\, stress-responsive functions\, and direct detection of archaeal tRNA modifications. The first chapter maps this unexplored landscape by applying Ordered Two-Template Relay sequencing (OTTR-seq) across nine archaeal species from diverse and extreme environments. This comparative analysis revealed previously unrecognized\, coordinated modification patterns\, including mutually exclusive methylation patterns in the acceptor stem of hyperthermophiles. Additional comparisons revealed co-evolution of tRNA modifying enzymes\, demonstrating how their domain architectures and substrate specificities have diverged to shape lineage-specific adaptations. Building on these evolutionary observations\, the second chapter investigates the functional role of tRNA modifications in the stress responses of the model halophile Haloferax volcanii. This work reveals how tRNA modification dynamics might balance structural stability against flexibility to manage stress\, focusing on N2\,N2-dimethylguanosine (m22G) at position 26. By integrating tRNA sequencing\, proteomics\, and codon usage data within a linear mixed-effects model\, this work quantifies how the m22G modification status fine-tunes the translation of specific\, codon-biased genes\, establishing it as a modulator of the adaptive stress response. Addressing limitations of reverse-transcription (RT)-based sequencing methods for detecting modifications\, the final chapter explores the use of direct RNA nanopore sequencing. The focus is on archaeosine (G+)\, a modification unique to Archaea that is inaccessible to RT-based sequencing methods. The resulting custom model accurately detects archaeosine in its native species. However\, cross-species comparisons reveal significant challenges with species-specific overfitting\, providing insights into development of universally applicable modification callers. \nEvent Host: Jesse Leavitt\, Ph.D Candidate\, Biomolecular Engineering & Bioinformatics
URL:https://events.ucsc.edu/event/leavitt-j-bmeb-evolutionary-dynamics-functional-adaptations-in-stress-response-and-direct-detection-of-trna-modifications-in-archaea/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
GEO:46.1226939;-64.7891251
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250808T140000
DTEND;TZID=America/Los_Angeles:20250808T140000
DTSTAMP:20260502T012304
CREATED:20250801T070000Z
LAST-MODIFIED:20250925T231427Z
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SUMMARY:Katte\, P. (BMEB) - Interactive and Scalable Frameworks for Pathogen Surveillance and Ancestral Recombination Graph
DESCRIPTION:The explosive growth of genomic data\, driven by advances in sequencing and inference technologies\, presents both an opportunity and a challenge for evolutionary biology and public health. Existing visualization and analysis tools often fall short in handling the scale\, complexity\, and uncertainty of modern genomic datasets—especially in the areas of pathogen surveillance and ancestral recombination inference. This thesis introduces new tools that provide scalable visualization and analysis to bridge these gaps and enable more interpretable and actionable genomic insights. \nFirst\, I develop an interactive dashboard within a tool called WEPP for wastewater-based pathogen surveillance. It combines phylogenetic placement with intuitive web-based visualization\, allowing public health officials to track variant spread at high resolution. Second\, I build Lorax\, a browser-based platform for visualizing Ancestral Recombination Graphs (ARGs) at biobank scale. Lorax incorporates a multi-agent system that supports natural language querying\, code generation\, and interactive tree exploration. Finally\, I introduce a novel inference framework based on Generative Flow Networks to sample from posterior distributions over ARGs\, addressing key limitations in uncertainty quantification and scalability found in existing methods. Together\, these tools aim to make the study of evolution and disease more accessible and effective\, helping researchers and public health teams draw clearer conclusions from complex genetic data. \nEvent Host: Pratik Katte\, PhD Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Russ Corbett-Detig
URL:https://events.ucsc.edu/event/katte-p-bmeb-interactive-and-scalable-frameworks-for-pathogen-surveillance-and-ancestral-recombination-graph/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
GEO:46.1226939;-64.7891251
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