1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请
联系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
计算生物学/Computational Biology
2.
授课院系
Originating Department
生物系/ Department of Biology
3.
课程编号
Course Code
BIO309
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业核心课 （生物信息专业）
Major Core Courses (Bioinformatics Major)
专业选修课 （生物科学、生物技术专业、生物医学工程专业）
Major Elective Courses (Bioscience、Biotechnology、Biomedical Engineering Majors)
6.
授课学期
Semester
秋季 Fall
7.
授课语言
Teaching Language
中英双语 English & Chinese
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
all instructors）
靳文菲,生物系
Dr. Wenei JIN, Department of Biology
jinwf@sustech.eud.cn
0755-88018478
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
待公布 To be announced
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
2
授课方式
Delivery Method
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
11.
学时数
Credit Hours
32
64
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
BIO306 生物信息学 /Bioinformatics
14.
其它要求修读本课程的学系
Cross-listing Dept.
None 无
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
计算生物学是利用数学，计算机科学和统计学的方法解决生物学问题的交叉学科。该学科利主要用生物数据开发模型来理
解生物医学问题。它是研究遗传学、进化、基因组学、流行病学和系统生物学的一种重要实用工具。该课程是生物信息学
专业的必修课，是其它生物学相关专业的选修课。该课程旨在帮助学生理解有关基因组、序列分析、生物数据库、疾病关
联基因和进化分析的主要问题。将对现有各种方法进行批判性描述，讨论每种方法的优点和局限性，并使用相关工具进行
实践练习。该课程将同时培养学生积极的科学精神、激发他们的科学好奇心。先修课程包括分子生物学入门课程或讲师许
可。
Computational biology is an interdisciplinary subject that uses mathematical, computer science and statistical methods to
solve biological problems. This subject mainly focuses on using biological data to develop models to understand
biomedical issues. It is a practical, hands-on tool to study genetics, evolution, genomics, epidemiology and systems
biology. It is a mandatory course for Bioinformatics major, and elective course for all other majors related to biology. It is
designed to help students to understand the major issues concerning genomics, analysis of sequence, biological
database, disease associated gene and evolution. Various existing methods will be critically described and the strengths
and limitations of each will be discussed, with practical assignments utilizing the tools. It is to train students’ vigorous
Scientific Spirit and inspire their scientific curiosity. Prerequisites include an introductory molecular biology course or
permission of the instructor.
16.
预达学习成果 Learning Outcomes
本课程完成后，学生将能够：
（1）熟悉计算生物学领域常用数据分析方法。掌握生物信息常用 linux 命令，序列比对原理和方法，数据降维和聚类的原
理和方法，进化分析的原理和方法。
（2）能够独立对生物序列数据进行基本生物信息分析。
（3）对计算生物学产生更浓厚的兴趣。理解计算生物学交叉学科的特点，发展的动力和前景。
With the completion of this course, the students will
(1) Understand the common data analytical approaches in computational biology. Master the Linux commands
commonly used in bioinformatics, the principles and methods of sequence alignment, the principles and methods of data
dimensionality reduction and clustering, and the principles and methods of evolutionary analysis.
(2) Develop the capability of independently analyzing biological sequence data
(3) Become more interested in computational biology. Understand the characteristics, driving forces and prospects of
computational biology.
3
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明
主讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
1. Introduction of computational biology and course introduction 学时：2+2/Hours: 2+2
1. 计算生物学概要和课程概要
1.1 The emergence of computational biology/bioinformatics
1.1 计算生物学/生物信息学的出现
1.2 Development of computational biology
1.2 计算生物学的发展
1.3 Major topics in computational biology
1.3 计算生物学的主要话题
1.4 Computational biology and genomics
1.4 计算生物学和基因组学
1.5 Examples of Computational biology applications
1.5 计算生物学应用实例
1.6 Course Introduction: Goals, outline, evaluation/examination and learning guidelines
1.6 课程介绍：目标、大纲、评估/考试和学习建议
2. Basic computational skills (Linux + programing) 学时：2+2/Hours: 2+2
2. 计算机基础 （Linux+编程）
2.1 Linux system and Open Source Software: GitHub
2.1 Linux 系统和开源软件：Github
2.2 Terminal and basic Linux operations
2.2 终端和基本 Linux 操作
2.3 Introduction of programming languages for bioinformatics
2.3 生物信息学编程语言简介
2.4 Programming language Perl
2.4 编程语言 Perl
4
2.5 Programming language Python
2.5 编程语言 python
2.6 R Language Statistics and Drawing
2.6 R 语言统计与绘图
3 Shell Programming and bioinformatics 学时：2+2/Hours: 2+2
3 Shell 编程和生物信息学
3.1 Basic shell commands
3.1 基本 shell 命令
3.2 File system
3.2 文件系统
3.3 Managing data
3.3 管理数据
3.4 Output redirection
3.4 输出重定向
3.5 Software for Linux
3.5 Linux 软件
3.6 Biological data analysis: Modularization and pipeline
3.6 生物数据分析：模块化和流水线化
4 Human Genome and Human genome project (HGP) 学时：2+2/Hours: 2+2
4 人类基因组与人类基因组计划（HGP）
4.1 The basic information about human Genome
4.1 人类基因组的基本信息
4. 2 HGP initiation and sequencing Strategies
4.2 人类基因组计划的启动和测序策略
4.3 Completion of HGP and its findings
5
4.3 人类基因组计划完成和产生的结果
4.4 HGP greatly promote sequencing technologies and genomic study
4.4 人类基因组计划极大的促进了测序技术和基因组学研究
4.5 Post HGP era: HapMap, ENCODE, 1000 genome, 3D/4D genome
4.5 后基因时代：人类单体型计划，ENCODE, 千人基因组，三维/四位基因组
4.6 Precision medicine and personalized medicine
4.6 精准医学和个性化医学
4.7 Access the human genome database
4.7 人类基因组数据库访问
5 Pairwise sequence alignments 学时：2+2/Hours: 2+2
5 成对序列比对
5.1 Genomes change over time
5.1 基因组随时间变化
5.2 Sequence comparisons
5.2 序列比较
5.3 Dynamic programming alignment
5.3 动态规划算法比对序列
5.3.1 Global alignment (Needleman-Wunsch)
5.3.1 全局序列比对 (Needleman-Wunsch)
5.3.2 Local alignment (Smith-Waterman)
5.3.2 局部序列比对 (Smith-Waterman)
6 Sequence Similarity Searching 学时：2+2/Hours: 2+2
6 序列相似性搜索
6.1 Approximate alignment is fast
6.1 近似比对速度快