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X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20220525T113000
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DTSTAMP:20260517T170755
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SUMMARY:QCBio Research Seminar: Connor Razma (Hoffmann)\, BS/MS Student
DESCRIPTION:TITLE: “Baseline MEthylation Patterns prior to flu vaccination.” \nABSTRACT: Influenza affects millions worldwide each year with responses varying from individual to individual. Influenza can be broken down into subtypes specifically H1N1\, H3N2\, Yamagata\, and Victoria. One way to measure the immune response to influenza is to measure a person’s antibody response to influenza. To measure how many antibodies are present in a sample\, a hemagglutination inhibition assay (HAI) is used. DNA methylation is an epigenetic mechanism used to regulate gene expression in cells. Its mechanism of action is the addition of a methyl group to cytosine at a cytosine-guanine pair. DNA methylation has been shown to change in response to stimuli such as viral or bacterial infections. DNA methylation can be measured by bisulfite sequencing\, specifically reduced representation bisulfite sequencing in our case. In this study\, data was taken from patients who had the flu vaccination. Their antibody data was measured using the HAI assay by the University of Georgia and their methylation data was measured using reduced representation bisulfite sequencing by the Pellegrini and Reed lab at UCLA. Using various statistical learning algorithms we were able to find methylated sites that were good predictors of vaccine response. Elastic net regression proved to be a particularly good predictor of vaccine response\, and after further analysis\, it was revealed that the best prediction happened with only a few significant sites. Some of these significant sites seem to be involved in regulating immune response and membrane function. Further work will be done to determine the prediction accuracy of these algorithms with just these sites. Ideally\, after this future work and other experiments\, these methylated sites can be used as biomarkers to indicate response to flu vaccination. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Connor-Razma-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-connor-razma-hoffmann-undergraduate-bioinformatics-researcher/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/05/281170460_1052299975682517_1062133480470997117_n-copy.jpg
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DTSTAMP:20260517T170755
CREATED:20220517T233629Z
LAST-MODIFIED:20220602T000036Z
UID:21686-1653480000-1653481800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Ha Vu (Ernst)\, Graduate Student in Bioinformatics
DESCRIPTION:TITLE: “Universal annotation of the human genome through integration of over a thousand epigenomic datasets.” \nABSTRACT: Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome\, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative ‘stacked modeling’ approach was previously suggested\, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type\, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.\nUsing a version of ChromHMM enhanced for large-scale applications\, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types\, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations\, which we use in characterizing each state. Compared to per-cell-type annotations\, the full-stack annotations directly differentiate constitutive from cell type specific activity and is more predictive of locations of external genomic annotations.\nThe full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Ha-Vu-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-ha-vu-ernst-graduate-student-in-bioinformatics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
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