INVITED SPEAKER

 

 

Dr. Dongyu Zhao

Peking University, China

 

Dr Dongyu Zhao graduated with a PhD in Bioinformatics from Beijing Institute of Genomics, Chinese Academy of Sciences. He performed Bioinformatics research at his postdoctoral stage at Houston Methodist Research Institute and Boston Children’s Hospital in the USA. Dr Zhao joined Peking University as an Assistant Professor to start his own independent lab in 2021. His research interest involves computational modelling of genetics and epigenetic mechanisms for cell identity regulation in disease, with a particular interest in tumorigenesis. Dr. Zhao’s researches have laid ground for 20+ publications in top journals, including his first-author papers in Molecular Cell 2018, Nature Communications 2018 & 2020(2), Oncogene 2020. Due to his academic contributions, he received the Moran Foundation Award of Houston Methodist Hospital in 2018.

 

Speech Title: "CHD6 Promotes Prostate Cancer by Linking Broad Nucleosome Eviction to Transcriptional Activation of Cancer Pathways"

 

Abstract: Despite of being a member of the chromodomain helicase DNA-binding protein family, little is known about the exact role of CHD6 in chromatin remodeling or cancer disease. Here we show that CHD6 is an oncogene to promote broad nucleosome eviction for transcriptional activation of many cancer pathways. We found CHD6 expression elevated in prostate cancer and associated with poor prognosis. Knockdown of CHD6 impaired oncogenicity of prostate cancer cells and tumor development in murine xenograft model. Ectopically overexpressing CHD6 induced neoplastic transformation of benign prostate epithelial cells. Mechanistically, we found that the binding of CHD6 on chromatin was required to evict nucleosomes from promoters and gene bodies for transcriptional activation of many oncogenic pathways. In particular, we found the RB1-E2F1 pathway in prostate cancer relied on the CHD6 function. These results demonstrated an oncogenic function of CHD6 in evicting nucleosomes for transcriptional activation of prostate cancer pathways.

 

 

Asst. Prof. Hangjin Jiang

Zhejiang University, China

 

Hangjin Jiang is an assistant Professor at Center for Data Science, Zhejiang University. Before joining in Zhejiang University, he obtained his Ph.D in statistics at 2018 from Department of Statistics, The Chinese University of Hong Kong. During this Ph.D study, he visited Prof. Wing Hung Wong at department of Statistics, Stanford University. Now, he mainly works on developing statistical methods for analyzing data from different areas, such as biology and medicine. His research interests including Biostatistics/Computational Biology, Bayesian Data Analysis and Statistical/Deep Learning, and his works has been published in journals such as PLoS Computational Biology, Briefings in Bioinformatics, Methods and Statistica Sinica.

 

Speech Title: "Association Analysis in Biological Problems"

 

Abstract: Exploring the relationship between factors of interest is a fundamental step for further analysis on various scientific problems such as understanding the genetic mechanism underlying specific disease, brain functional connectivity analysis. There are many methods proposed for association analysis and each has its own advantages, but none of them is suitable for all kinds of situations. This brings difficulties and confusions to practitioner on which one to use when facing a real problem. In this work, we propose to combine power of different methods to detect associations in large data sets. It goes as combining the weaker to be stronger. Numerical results from simulation study and real data applications show that our new framework is powerful.

 

Prof. Lei Li

Shenzhen Bay Laboratory, China

 

Dr. Li is Professor of Institute of Systems and Physical Biology at Shenzhen Bay Laboratory, Shenzhen Overseas High-level Talent (Class B). His research focuses on computational modeling of transcription and epigenetic regulation for complex diseases. Dr. Li has published 34 high-impact peer-reviewed papers with a cumulative impact factor of > 350, including 9 papers as the (co)-first or (co)-corresponding authors in prestigious journals such as Nature Genetics, Molecular Cell and Nucleic Acids Research. In the past 5 years, the papers have been cited by > 1000 times, and has an H-index of 18. Dr. Li is also the reviewer for journals such as Molecular Psychiatry, Nucleic Acids Research, Genome Research, etc.

 

Speech Title: "Immune-Response 3′UTR Alternative Polyadenylation Quantitative Trait Loci Contribute to Variation in Human Complex Traits and Diseases"

 

Abstract: Genome-wide association studies (GWASs) have identified thousands of non-coding variants that are associated with human complex traits and diseases. Alternative polyadenylation (APA) is a key post-transcriptional modification for most human genes that substantially impacts upon cell behavior. Here, we mapped 8,862 3′-untranslated region APA quantitative trait loci in 18 human immune baseline cell types and 8 stimulation conditions (immune 3′aQTLs). Co-localization and mendelian randomization analyses of immune 3′aQTLs identified 376 genes where 3′aQTL are associated with variation in complex traits, 36.1% of which were derived from response 3′aQTLs. Overall, these analyses reveal the role of immune 3′aQTLs in the determination of complex traits, providing new insights into the regulatory mechanisms underlying disease etiologies.

 

Dr. Xin Sheng

Zhejiang University Medical Center, China

 

Dr. Xin Sheng, a Principal Investigator from the Zhejiang University Medical Center. For the last five years, Dr. Sheng has made discoveries fundamental towards defining critical genes, cell types and mechanisms of chronic kidney disease, and developed a comprehensive mammalian transcriptomic database, MTD. She has published high-quality papers as a first or co-first author at Nature Genetics, Science Translational Medicine, PNAS, Nucleic Acids Research, Briefings in Bioinformatics in the last 5 years. Her research interests focus on investigating the effect of genetic variations on transcriptome and epigenome to prioritize the potential causal genes, cell types for chronic kidney disease. Now her lab at ZJU is recruiting bioinformatics and web lab postdocs.

 

Speech Title: "Mapping the Genetic Architecture of Human Traits to Cell Types in the Kidney Identifies Mechanisms of Disease and Potential Treatments"

 

Abstract: The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.

 

Prof. Yungang Xu

Xi’an Jiaotong University, China

 

Yungang Xu, PhD, Professor of Xi’an Jiaotong University, China. He earned his Ph.D. from the School of Computer Science and Technology at the Institute of Technology (HIT), China. He also holds Bachelor's and Master's degrees in Biology. Between January 2015 and February 2021, he moved to the U.S.A and worked as a Postdoctoral Research Fellow and Research Assistant Professor at Wake Forest University, UT Health, and the Children’s Hospital of Philadelphia & University of Pennsylvania, successively. His research interest covers bioinformatics, machine learning, epigenetics & transcriptional regulation, and single-cell omics. He has published more than 20 papers in journals of his field, such as Genome Biology, AJHG, Nucleic Acids Research, Briefings in Bioinformatics, and Bioinformatics. Specifically, he dedicates himself to translating big genomic data, epigenomic data, and transcriptomic data into scientific insights and clinical knowledge. He has been awarded one provincial and one departmental science and technology award from the Chinese government. He hosted one general project of the National Natural Science Foundation of China (NSFC) and participated in four NSFC projects as well as four NIH projects as the core member. He is the editor of multiple journals and was the committee member of multiple international conferences.

 

Speech Title: "Generative Adversarial Networks for Single-cell RNA-seq Imputation"

 

Abstract: Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100 000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.

 

 

 

 

 

 


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