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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:20211027T120000
DTEND;TZID=America/Los_Angeles:20211027T123000
DTSTAMP:20260518T005156
CREATED:20211017T155101Z
LAST-MODIFIED:20211029T185848Z
UID:19702-1635336000-1635337800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Giovanni Quinones Valdez (Xiao)\, Grad student in Bioengineering
DESCRIPTION:TITLE: “scAllele\, a versatile tool for the detection and analysis of variants in scRNA-seq.” \nABSTRACT: Single-cell RNA sequencing (scRNA-seq) data contain rich information at the gene\, transcript\, and nucleotide levels. Most analyses of scRNA-seq have focused on gene expression profiles\, and it remains challenging to extract nucleotide variants and isoform-specific information. Here\, we present scAllele\, an integrative approach that detects single nucleotide variants\, insertions\, deletions\, and their allelic linkage with splicing patterns in scRNA-seq. We demonstrate that scAllele achieves better performance in identifying nucleotide variants than other commonly used tools. The read-specific variant calls by scAllele enables allele-specific splicing analysis. Applied to a lung cancer scRNA-seq data set\, scAllele identified variants with strong allelic linkage to alternative splicing\, some of which being cancer-specific. scAllele represents a versatile tool to uncover multi-layer information and novel biological insights from scRNA-seq data. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Giovanni-Valdez.mp4\n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-giovanni-quinones-valdez-xiao-grad-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/14/2021/10/Giovanni-Quinones-valdez.jpg
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DTSTART;TZID=America/Los_Angeles:20211027T123000
DTEND;TZID=America/Los_Angeles:20211027T130000
DTSTAMP:20260518T005156
CREATED:20211018T180755Z
LAST-MODIFIED:20211029T190003Z
UID:19713-1635337800-1635339600@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Ulzee An (Sankararaman)\, Grad student in Computer Science
DESCRIPTION:TITLE: “AutoComplete: Deep Learning-based Phenotype Imputation” \nABSTRACT: Health data has become increasingly available\, vast in scale\, and highly missing. For many downstream applications\, the ability to accurately impute missing features in health records may tap into additional analytical power which would be unrealized otherwise. While existing imputation methods are applicable\, many fall short in one or more aspects of being reliable or scalable in the domain of massive\, highly incomplete\, and heterogenous population-scale data. We propose AutoComplete\, a deep learning-based imputation method that extends with ease to incomplete datasets with millions of entries and handles heterogeneous data of continuous and categorical format. In imputing phenotypes for a collection of half-million individuals from the UK Biobank\, AutoComplete significantly improved imputation accuracy for several phenotypes in comparison to best-performing low-rank factorization and deep learning methods. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Ulzee-An.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-ulzee-an-sankararaman-grad-student-in-computer-science/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/10/Ulzee-An.jpeg
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