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X-ORIGINAL-URL:https://bioinformatics.ucla.edu
X-WR-CALDESC:Events for UCLA | Bioinformatics
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DTSTART:20150101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20160502T160000
DTEND;TZID=UTC:20160502T170000
DTSTAMP:20160428T235937Z
CREATED:20160428T235937Z
LAST-MODIFIED:20160428T235937Z
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SUMMARY:Liang Chen Seminar
DESCRIPTION:Liang Chen\, Ph.D. \nAssociate Professor\, Department of Biological Sciences\, University of Southern California \n“Tackling overdispersion in RNA-seq data analysis” \nThe rapid advances in high-throughput sequencing technologies provide us an opportunity to dissect transcriptomes with unprecedented resolution. However transcriptome quantification is still hindered by non-uninform read sampling. Existing methods assume a constant bias factor for each relative position of genes or simply correct the sequence-specific bias caused by random hexamer priming. However\, the overall bias is complicated and caused by multiple factors including many unknown ones\, and the bias pattern can vary significantly across different regions and different protocols. In light of these facts\, we proposed to use the generalized-Poisson (GP) model to estimate the bias in a data-adaptive way without any presumption. We further incorporated this data-adaptive bias correction in the deconvolution of isoform expression. Our methods significantly improve the quantification of isoform and gene expression as well as the derived exon inclusion rates. For single-cell RNA-seq data\, our method distinguishes bias heterogeneity from true biological heterogeneity and uncovers smaller cell-to-cell expression variability. \n  \n  \n 
URL:https://bioinformatics.ucla.edu/event/liang-chen-seminar/
LOCATION:Boyer Hall 159
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BEGIN:VEVENT
DTSTART;TZID=UTC:20160516T160000
DTEND;TZID=UTC:20160516T170000
DTSTAMP:20160524T231528Z
CREATED:20160524T231528Z
LAST-MODIFIED:20160524T231528Z
UID:1123-1463414400-1463418000@bioinformatics.ucla.edu
SUMMARY:Hongkai Ji Seminar
DESCRIPTION:Hongkai Ji\, Ph.D. \nAssociate Professor\, Department of Biostatistics\, John Hopkins University \n\nTitle: Genome-wide Prediction of DNase I Hypersensitivity Using Gene Expression\n\nAbstract: We evaluate the feasibility of using a biological sample’s transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD\, Big Data Regression for predicting DH\, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Element (ENCODE) data\, we found that gene expression to a large extent predicts DH\, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element’s neighboring genes. We show that the predicted DH predicts transcription factor binding sites (TFBSs)\, prediction models trained using ENCODE data can be applied to gene expression samples in Gene Expression Omnibus (GEO) to predict regulome\, and one can use predictions as pseudo-replicates to improve the analysis of high-throughput regulome profiling data. Besides improving our understanding of the regulome-transcriptome relationship\, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.\n 
URL:https://bioinformatics.ucla.edu/event/hongkai-ji-seminar/
LOCATION:Boyer Hall 159
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BEGIN:VEVENT
DTSTART;TZID=UTC:20160523T160000
DTEND;TZID=UTC:20160523T170000
DTSTAMP:20160518T162844Z
CREATED:20160518T162844Z
LAST-MODIFIED:20160518T162844Z
UID:1736-1464019200-1464022800@bioinformatics.ucla.edu
SUMMARY:Su-In Lee Seminar
DESCRIPTION:Su-In Lee\, Ph.D. \nAssistant Professor of Computer Science and Engineering\, and Genome Sciences\, University of Washington \n“Learning the human chromatin network from all ENCODE ChIP-seq data” \nAbstract: \nIntroduction: A cell’s epigenome arises from interactions among regulatory factors — transcription factors\, histone modifications\, and other DNA-associated proteins — co-localized at particular genomic regions.  Identifying the network of interactions among regulatory factors\, the chromatin network\, is of paramount importance in understanding epigenome regulation. \nMethods: We developed a novel computational approach\, ChromNet\, to infer the chromatin network from a set of ChIP-seq datasets.  ChromNet has four key features that enable its use on large collections of ChIP-seq data.  First\, rather than using pairwise co-localization of factors along the genome\, ChromNet identifies conditional dependence relationships that better discriminate direct and indirect interactions.  Second\, our novel statistical technique\, the group graphical model\, improves inference of conditional dependence on highly correlated datasets.  Such datasets are common because some transcription factors form a complex and the same transcription factor is often assayed in different laboratories or cell types.  Third\, ChromNet’s computationally efficient method allows joint network learning across across 115 cell types\, which greatly increases the scope of possible interactions. Finally\, the genomic context causing any network edge can be inferred to aid understanding. \nResults: We applied ChromNet to all available ChIP-seq data from the ENCODE Project\, consisting of 1\,451 ChIP-seq datasets\, which revealed previously known physical interactions better than alternative approaches.  ChromNet also identified previously unreported regulatory factor interactions.  We experimentally validated one of these interactions\, between the MYC and HCFC1 transcription factors. \nDiscussion: ChromNet provides a useful tool for understanding the interactions among regulatory factors and identifying novel interactions.  We have provided an interactive web-based visualization of the full ENCODE chromatin network and the ability to incorporate custom datasets at http://chromnet.cs.washington.edu. \nBio:  Professor Su-In Lee is an Assistant Professor in the Departments of Computer Science & Engineering and Genome Sciences at the University of Washington. She received her Ph.D. degree in Electrical Engineering from Stanford University in 2009. Before joining the UW in 2010\, she was a Visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University. \nHer interest is in developing advanced machine learning (ML) algorithms to analyze high-throughput molecular data 1) to discover molecular mechanisms of disease initiation and progression\, 2) to identify therapeutic targets\, and 3) to develop personalized therapy based on individual patients’ molecular profiles. She has been named an American Cancer Society Research Scholar in 2015 and received the NSF CAREER award in 2016. Her lab is currently funded by the American Cancer Society\, the National Institutes of Health\, the National Science Foundation\, the Institute of Translational Health Sciences and the Solid Tumor Translational Research. \n  \n 
URL:https://bioinformatics.ucla.edu/event/su-in-lee-seminar/
LOCATION:Boyer Hall 159
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BEGIN:VEVENT
DTSTART;TZID=UTC:20160524T170000
DTEND;TZID=UTC:20160524T180000
DTSTAMP:20160524T231629Z
CREATED:20160524T231629Z
LAST-MODIFIED:20160524T231629Z
UID:1842-1464109200-1464112800@bioinformatics.ucla.edu
SUMMARY:UCLA Undergraduate Bioinformatics Minor Information Session
DESCRIPTION:The UCLA Undergraduate Bioinformatics Minor program encourages all students currently enrolled as program minors as well as those who may be interested in learning more about the Bioinformatics minor as well as research opportunities in Bioinformatics to attend our quarterly information session on Tuesday\, May 24th at 5-6pm in Boelter Hall 4760. \nProgram faculty as well as current Bioinformatics undergraduate students involved in research will host this town hall style meeting to provide information about the bioinformatics minor and information on how to get involved in Bioinformatics research projects at UCLA. \nLight refreshments will be provided.
URL:https://bioinformatics.ucla.edu/event/ucla-undergraduate-bioinformatics-minor-information-session/
LOCATION:Boelter Hall 4760
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