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Mini Symposium from Key Lab of PICB

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时间:2017-09-13  来源:文本大小:【 |  | 】  【打印

Time : 1:00-4:00 pm , Sep 18, (Monday )
Venue: Room 300, SIBS Main Building, Yueyang Road 320
Host:Prof. Jingdong Jackie Han

           CAS-MPG Partner Institute for Computational Biology

 

1.Speaker: Dr. Chongzhi Zang,

                     Assistant Professor, Center for Public Health Genomics, University of Virginia

Title:Modeling Gene Regulation with Public ChIP-seq Data: from Integration to Prediction

Abstract:

        Epigenetic regulation of gene expression plays a critical role in many biological processes including cancer formation and progression. Prediction of enhancers and transcription factors (TFs) regulating differential expression gene sets is an essential problem in functional genomics research. In this talk I will present a series of computational approaches for modeling gene regulation using massive publicly-available data from human and mouse. We develop MARGE (Model-based Analysis of Regulation of Gene Expression), a logistic regression and semi-supervised learning-based approach for predicting genomic cis-regulatory profiles that regulate any given gene set by leveraging over a thousand public H3K27ac ChIP-seq datasets. We develop BART (Binding Analysis for Regulation of Transcription) to predict TFs associated with MARGE-predicted cis-regulatory profiles using thousands of TF ChIP-seq datasets. Integrating these approaches on The Cancer Genome Atlas (TCGA) molecular profiling data, we reconstruct the functional enhancer profiles and predict active transcription factor (TF) targets for each TCGA cancer type, presented in the Cistrome Cancer web resource. Our results demonstrate the power of utilizing public data for computational studies of gene regulation and epigenetics.

 

2.Speaker:Dr Bo Li,

                   Dana-Farber Cancer Institute, Harvard School of Public Health, Postdoctoral Fellow

Title:Computational approaches for dissecting tumor-immune interaction

Abstract:

        Characterization of the interaction between cancer and immune system is critical to developing novel immunotherapies. Here in this talk, I will present two computational methods I have developed. The first one is Tumor IMmune Estimation Resource, or TIMER (https://cistrome.shinyapps.io/timer/), which is a statistical tool for deconvolving different immune cell components in the tumor microenvironment using gene expression data. The second one is called T-cell receptor Repertoire Utilities for Solid Tumor, or TRUST (https://bitbucket.org/liulab/trust), which is a de novo assembler for analyzing the TCR hypervariable sequences using unselected RNA-seq data. TRUST has the highest sensitivity of all the competing algorithms. Application of both methods to large cancer cohort lead to biological findings with potential clinical applications.

 

3.Speaker:Dr Wei Li,

                   Dana-Farber Cancer Institute, Harvard School of Public Health, Postdoctoral Fellow

Title:CRISPR Screens: Algorithms and Applications to Functional Genomics

Abstract:

        High-throughput CRISPR/Cas9 knockout screens have shown great promise in the functional studies of coding genes and non-coding elements. In this talk, I will discuss my recent work to develop algorithms for modeling CRISPR screening data, and identify functional coding and non-coding elements in cancer cells. I present MAGeCK and MAGeCK-VISPR, two novel algorithms to analyze CRISPR screens. MAGeCK uses a ranked-based method to identify top hits in the screen, while MAGeCK-VISPR builds a maximum-likelihood framework to analyze screens involving multiple conditions. We demonstrated the application of both algorithms in studying the functions of coding and non-coding elements in cancer. Specifically, we studied the mechanism of endocrine resistance, where cancer cells are resistant to the standard endocrine therapy in many breast cancer patients. We identified and validated genes whose loss confers endocrine resistance, as well as synthetic lethal vulnerabilities that are potential drug targets. These findings demonstrate the potential of CRISPR screens to tackle many emerging problems, including cancer and drug resistance.

 

       All are welcome!

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