基因组学前沿和精准医学

课程编码：1801010710I0P1003Y 英文名称：The Frontier of Genomics and Precision Medicine 课时：51 学分：2.50 课程属性：学科核心课 主讲教师：张治华等

"Epigenome and Genome structures. This section introduces the concept of “epigenetics” and the structures of chromatins. It covers chromatin remodeling and the modifications of DNA and histones, and their roles in chromatin structure maintenance. Contemporary life sciences and medicine are moving towards the era of large data as represented by high-throughput sequencing. How to model, analyze and interpret genomic data will determine whether we can quickly and accurately discover new biological phenomena and rules, and provide accurate medical care for patients. This course will introduce common data types in genomics, such as DNA-seq, RNA-seq, and statistical analysis and graphing methods commonly used in data analysis.

Proteomics is a fast and powerful discipline aimed at the study of the whole proteome or the sum of all proteins from an organism, tissue, cell or biofluid, or a subfraction thereof, resulting in an information-rich landscape of expressed proteins and their modulations under specific conditions. In the section for proteomics, we will introduce the most common technologies and workflows used in proteomic studies. Details of this section include principles and techniques in proteomics, recent advances in proteomics and application of proteomic technology. In the section for protein phase separation, we will introduce physical basis of phase separation, biological functions of phase separation and computational screening of biological phase-separating proteins.

In the section for genetics and genomics studies, we will introduce technologies used to understand the genetic architectures underlying human complex traits, disorders, and diseases. These include genome-wide association studies, next-generation sequencing technologies, as well as the computational methods used to mine and interpret the genetics and genomics data. We will introduce approaches for basic association studies to identify disease-associated loci, including common variants, rare variants, and de novo mutations. We will also introduce approaches for advanced analyses widely used in the post-GWAS era to interpret and prioritize causal variants. These include integrative methods that rely on multi-omics data (such as transcriptome data and epigenomics data), deep learning methods to fine map genetic variants, and statistics methods to understand the regulatory roles and functional impacts of genetic variants in disease-relevant contexts.

In the last section, we mainly talks about computational cancer biology, but with a highlight on the implication of computational analysis in solving practical problem in cancer and the development of related treatment. We will first introduce the concept of tumor intra heterogeneity which is the main reason of drug resistance to standard treatment. We will discuss computational approaches for understanding tumor intrinsic subtyping, clonal evolution as well as therapeutic implications. A big part of the course will focus on cancer transcriptomics including canonical gene expression, non-coding elements, regulatory network and mRNA splicing. We will summarize computational methods and current challenges in cancer splicing analysis. Splicing factors are recurrently mutated in human cancers, which provide genetic evidence directly linking RNA splicing dysregulation to tumorigenesis. We will particularly talk about spliceosomal mutations in human cancer and therapeutic targeting of those mutations. Lastly, we will introduce pan-cancer analysis which takes advantage of the increasing amount of genomic data and human cancer projects, and cancer pharmacogenomics studies towards a speed-up of translational medicine."

Molecular biology, Statistics, Computer programming

第一章 Transcriptome 3.0学时 张治华
第1节 1 Basic principle of transcription;
第2节 2 Identification of transcription regulatory factor binding sites
第3节 3 Transcription regulatory factor binding sites and human disease
第二章 Epigenome 3.0学时 张治华
第1节 4 Promoter and enhancer identification methods
第2节 5 DNA methylation, histone Modification and Gene expression Regulation
第3节 6 Basic experimental techniques for Noncoding RNA
第4节 7 Noncoding RNA and Human Disease
第三章 3D genome 5.0学时 张治华
第1节 8 Basic experimental techniques for 3D Genome
第2节 9 Methods for recognition of Compartment A/B and TAD
第3节 10 Reconstruction of the 3D genomic structure
第4节 11 Chromatin loop identification methods based on multi-omics approach
第5节 12. 3D genomics and human disease
第四章 Basic Concepts of population Genetics I 3.0学时 张治华
第1节 13 Introduction to Gene Regulatory Network
第2节 14 Basic Concepts of population Genetics I
第3节 15 Basic Concepts of population Genetics II.
第五章 Proteomics: technologies and their applications 3.0学时 李婷婷
第1节 Proteomics: principles and techniques
第2节 Recent advances in proteomics
第3节 Application of proteomic technology
第六章 Biological function and computational analysis of phase separation 3.0学时 李婷婷
第1节 Physical basis of phase separation
第2节 Biological functions of phase separation
第3节 Computational screening of biological phase-separating proteins
第七章 Genomics: introduction of GWAS and complex diseases 3.0学时 贾佩林
第1节 Principles of GWAS: common variants, rare varaints, and de novo mutations
第2节 Techniques: array based and next-generation sequencing
第3节 Trends in genomics data analysis
第八章 Genomics: post-GWAS analysis I 3.0学时 贾佩林
第1节 Gene-based analysis
第2节 Set-based analysis
第3节 Regulatory roles of genetic variants: QTL
第九章 Biobank and resource 3.0学时 贾佩林
第1节 GWAS Catalog, 1KGP, UK10K
第2节 GTEx: tissue transcriptomes and eQTL
第3节 Roadmap and ENCODE
第十章 Genomics: post-GWAS analysis II 3.0学时 贾佩林
第1节 Colocalization analysis of GWAS data
第2节 Summary-based Mendelian Randomization
第3节 Transcriptome-wide association study (TWAS)
第十一章 Machine learning and deep learning to mine genetic variants 3.0学时 贾佩林
第1节 Regulatory elements and epigenomics
第2节 Convolutional Neural Network in analysing sequence data I
第3节 Convolutional Neural Network in analysing sequence data II
第十二章 Tumor heterogeneity 3.0学时 刘肇祺
第1节 Tumor intrinsic subtyping
第2节 Tumor clonal evolution
第3节 Therapeutic implications of tumor heterogeneity
第十三章 Cancer transcriptomics 3.0学时 刘肇祺
第1节 Gene expression study
第2节 Non-coding RNA in cancer
第3节 Regulatory network analysis
第十四章 Altered splicing in cancer 3.0学时 刘肇祺
第1节 mRNA splicing and dysregulation in cancer
第2节 Computational deciphering of splicing dysregulation
第3节 Computational challenges in cancer splicing analysis
第十五章 Spliceosomal mutations in cancer 3.0学时 刘肇祺
第1节 Spliceosomal mutations in cancer
第2节 Review of cancer splicing studies
第3节 Therapeutic targeting of RNA splicing
第十六章 Pan-cancer analysis and pharmacogenomics 3.0学时 刘肇祺
第1节 Human cancer projects and cell line based drug screening systems
第2节 Computational analysis reveal pan-cancer similarities and tumor-specific characteristics
第3节 Pharmacogenomic studies by patient-tumor-derived short-term cultures

Prof. ZHANG Zhihua et al.