课程大纲

COURSE SYLLABUS

课程代码

名称

Course Code/Title

⽣物医学组学数据分析 / Biomedical Omics Data Analysis

课程性质

Compulsory/Elective

专业选修课 / Elective

课程学分

学时

Course Credit/Hours

3/48

授课语言

Teaching Language

英⽂或中英 / English or Bilingual Teaching

授课教师

Instructor(s)

帅世⺠（医学院）/ Shimin Shuai (School of Medicine)

是否面向本科生开放

Open to undergraduates

or not

否 / No

先修要求

Pre-requisites

本科⽔平的分⼦⽣物学、遗传学、细胞⽣物学或近似⽣物学课程。推荐有⼀定的概

率论与数理统计知识、任意⼀⻔编程语⾔的⼊⻔经验，但不必须。

College-level molecular biology, genetics, cell biology or similar. Knowing basic

probability theory, statistics, and any entry-level programming is recommended

but not mandatory.

教学目标

Course Objectives

从⼈类基因组 2001 年问世以来，相关组学技术快速发展。⾯对测序获得的庞⼤数据，如何有效分析是不少科研⼈员在

实际研究中经常遇到的问题。《⽣物医学组学数据分析》专注于组学技术在⽣物医学研究中的应⽤。本课程强调实际

操作，旨在教会学⽣基本的组学数据分析流程。在学完本课程后，学⽣能掌握基本的 Unix 环境和 R 语⾔编程技巧，并

能使⽤ Shell 和 Bioconductor 来初步处理课题中碰到的常⻅组学数据（全基因组/外显组测序、RNA-Seq、单细胞

RNA-Seq、ChIP-Seq、ATAC-Seq 等）；或者能更好地知晓⾃⼰的分析需求，从⽽与⽣物信息学家能更⾼效地沟通。

除此之外，本课程还希望教会学⽣常⻅的数据分析⽅法背后的⼤致原理（不涉及算法细节），让学⽣了解相关数据分

析步骤背后的理由和逻辑。最后，本课程还希望教会学⽣如何更好地设计组学实验从⽽让之后的分析变得轻松、让结

论更可靠。

Since the advent of the human genome in 2001, related omics technologies have developed rapidly.

Facing the huge data obtained by sequencing, how to effectively analyze them are a problem that many

researchers often encounter in daily research. Biomedical Omics Data Analysis focuses on the

applications of omics technologies in biomedical research. This course also emphasizes hands-on

experience with the aim to teach students the basic omics data analysis workflow. After completing this

course, students are expected to master the basic Unix environment and R language programming skills

and can use Shell and Bioconductor to process common omics data encountered in their thesis research

(whole genome/exome sequencing, RNA-Seq, Single-cell RNA-Seq, ChIP-Seq, ATAC-Seq, long-read

sequencing etc.); or can better understand their analysis needs, so that they can communicate with

bioinformaticians more efficiently. In addition, this course also aims to teach students the general

principles behind common data analysis methods without getting into too many algorithmic details, so

that students can understand the reasoning and logics behind each analysis step. Finally, this course also

hopes to teach students how to better design omics experiments to make subsequent analysis easier

and make conclusions more reliable.

教学方法

Teaching Methods

课堂讲授、随堂演示、课后数据分析作业、期末项⽬（包含项⽬报告和 PPT 演示）。

Lectures, in-class demo, programming assignments, and final projects with written report and presentation.

10.

教学内容

Course Contents

Section 1

Overview of omics technologies / 组学技术概述

This section includes a general overview of the history, classification,

and application of modern omics technologies in biomedical research.

Section 2

Introduction to common omics data format / 常⻅组学数据格式简介

This section teaches data formats commonly used in omics data

analysis, such as FASTA, FASTQ, BAM/SAM/CRAM, GTF, BED,

Wig/Bigwig, VCF and TSV/CSV etc.

Section 3

Basics of the Unix environment / Unix 环境基础

This section introduces the Unix environment, such as how to log into

computing clusters, manipulate files, execute scripts, manage

permissions, and install softwares etc.

Section 4

Basics of R and Bioconductor / R 语⾔和 Bioconductor 基础

This section introduces the base R programming and how to use

Bioconductor to install and manage R packages related to omics data

analysis.

Section 5

RNA-Seq data analysis (I) / RNA 测序数据分析（I）

This section starts with introduction of the RNA sequencing

technology and then we will demonstrate how to analyze RNA-Seq data

from FASTQ files to aligned data.

Section 6

RNA-Seq data analysis (II) / RNA 测序数据分析（II）

This section will continue the RNA-Seq analysis workflow. We will

introduce commonly used downstream analyses such as differential

expression analysis, gene set over representation and enrichment

analysis etc.

Section 7

Single-cell RNA-Seq data analysis / 单细胞 RNA 测序数据分析

This section will introduce the general scRNA-Seq data analysis

workflow (data import, quality control, PCA/UMAP/tSNE, clustering,

marker gene discovery etc.) based on R package Seurat.

Section 8

WGS&WES data analysis / 全基因组和全外显组测序数据分析

This section will introduce DNA sequencing data analysis, focusing on

sequence alignments, variant calling, variant annotation, and association

analysis etc.

Section 9

ChIP-Seq data analysis / ChIP-Seq 数据分析

This section will introduce ChIP-Seq data analysis for transcription

factors and histone modifications. We will focus on how to align and

clean data, call and annotate peaks, find enriched sequence motifs etc.

Section 10

ATAC-Seq data analysis / ATAC-Seq 数据分析

This section covers the general data analysis workflow for ATAC-Seq,

including alignment, QC, peak finding, peak annotation, motif finding

etc. We will also compare ATAC-Seq to ChIP-Seq data analysis.

Section 11

Final project proposal presentation / 期末项⽬计划展示

Each student will be asked to prepare a short (~5 mins) presentation

showing the dataset and research question they will use in their final

projects.

Section 12

Long-read sequencing data analysis / ⻓读⻓测序数据分析

This section focuses on the emerging long-read technologies

(Nanopore and PacBio). We will introduce the benefits of long reads and

challenges of their analysis. We will also cover common tools used in

long-read data analysis.

Section 13

Multi-omics data integration / 多组学数据整合

This section focuses on how to integrate different types of omics data

computationally and demonstrates examples of such integrations in

literatures.

Section 14

Applications of omics in medical research / 组学在医学研究中的应⽤

This section shows active research areas that use different omics

technologies. Examples of recent studies will be discussed to

demonstrate the extensive usage of omics in biomedical research.

Section 15

Final project presentation (I) / 期末项⽬展示（I）

Students will be asked to give a presentation (10-15 mins per person)

about their final projects, including background, methods, results and

discussions.

Section 16

Final project presentation (II) / 期末项⽬展示（II）

Students will be asked to give a presentation (10-15 mins per person)

about their final projects, including background, methods, results and

discussions.

11.

课程考核

Course Assessment

1. 考核形式 Form of examination：本课采取期末项⽬报告+期末项⽬ PPT 展示的⽅式考核。The form of

examination will be final project report and presentation.

2. 分数构成 Grading policy

• 出勤 / Attendance 10%

• 课堂参与 / Participation 10%

• 课后作业 / Assignments 40%

• 期末项⽬报告+PPT 展示 / Final project report and presentation 40%

12.

教材及其它参考资料

Textbook and Supplementary Readings

No mandatory textbook for this course. We will recommend reading materials for each section during the

course.

Recommended resources for Unix and R:

- Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix

- Basic R concept tutorials: www.r-tutor.com/r-introduction

Recommended book for statistics:

- Modern Statistics for Modern Biology: free at https://www.huber.embl.de/msmb/

Recommended book for bioinformatics:

- The Biostar Handbook: not free at https://www.biostarhandbook.com/