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X-WR-CALNAME:Institute for Quantitative and Computational Biosciences
X-ORIGINAL-URL:https://qcb.ucla.edu
X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20220223T120000
DTEND;TZID=America/Los_Angeles:20220223T123000
DTSTAMP:20260517T210241
CREATED:20211207T223900Z
LAST-MODIFIED:20220223T222057Z
UID:20094-1645617600-1645619400@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Matthew Heffel (Luo)\, Graduate Student in Bioinformatics
DESCRIPTION:TITLE: “Multimodal Single-Cell Epigenomic Sequencing of the Developing Human Cerebral Cortex.” \nABSTRACT: Single cell epigenomic technologies allow the measurement of unique molecular signatures within cells\, however cell type complexity remains highly enigmatic. Emerging methods have enabled multiple modalities of epigenomic sequencing to be gathered from the same cell. Single-nucleus methyl-3C sequencing (sn-m3C-seq) delivers the capacity to capture chromatin conformation and DNA methylation information of 5’-methylcytosines with single cell fidelity. Developing neurons accumulate significant DNA methylation at non-CG sites (mCH)\, adjust patterns of CG methylation (mCG)\, and endure rearrangements of chromatin domains. These patterns of mCH\, mCG\, and chromatin interactions are specific to neuronal subtypes. We collected prefrontal cortex samples from 13 individuals at several developmental time stamps divided into four major age groups\, second trimester (2T)\, third trimester (3T)\, infant\, and adult. Applying sn-m3C-seq to our data of >29\,000 cells we identify 27 adult cell types and their developmental trajectories from five 2T cell types. The methylation features allow for intricate\, deeply specific cell type annotations and while the 3C modality highlights less specific cell types\, it can pick up forward trajectory signals that methylation either does not or that resolve as a single heterogeneous cell type in methylation; We observe developing glial progenitor cells identified in 3C that cluster as radial glia in the methylation feature space alone. The temporal forward stepping of the 3C feature set further allows us to validate cell types in different sample age groups. The integration of single cell modalities in sn-m3C-seq allows for the highly robust cell type classification as well as strong downstream analysis of epigenetic divergences of developing cell lineages. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Matthew-Heffel-edited.mp4\n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-matthew-heffel-luo-graduate-student-in-bioinformatics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/Matthew-Heffel.jpg
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DTSTART;TZID=America/Los_Angeles:20220223T123000
DTEND;TZID=America/Los_Angeles:20220223T130000
DTSTAMP:20260517T210241
CREATED:20211207T224209Z
LAST-MODIFIED:20220214T212656Z
UID:20099-1645619400-1645621200@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Chenghao (Trevor) Zhu (Boutros)\, Postdoc in Human Genetics
DESCRIPTION:TITLE: “moPepGen: a fast custom database generator from multi-omics data for proteogenomics.” \nABSTRACT: Cancers are driven by genomic variants such as SNV (single nucleotide variants) and INDEL\, often accompanied by many transcriptional variants. Modern mass spectrometry based proteomics is able to identify and quantify peptides and proteins comprehensively\, however the variant-harboring peptides that are absent in canonical databases are largely under-studied. A common problem in custom database construction is the large number of combinations of variants to consider. Thus major proteogenomic studies often choose a strategy that only captures peptides harboring a single variant. Existing algorithms also suffer from limited sources of variation. We developed moPepGen (multi-omics peptide database generator) that aims at accelerating proteogenomic researching by generating custom peptide database from variety of genomic and transcriptional variants. MoPepGen uses a graph-based algorithm that achieves a linear time complexity in integrating variants in contrast to the exponential complexity of exhaust searching. MoPepGen integrates variants from a variety of sources including SNV\, indel\, transcription fusion\, alternative splicing\, RNA editing\, and circRNA. It is able to generate peptides harboring any combinations of variants on the single peptide. MoPepGen is also highly extensible for other types of genomic and transcriptional variants. \n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-chenghao-trevor-zhu-boutros-postdoc/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/Chenghao-Trevor-Zhu-scaled.jpg
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