生物信息学课件中国科学院

1、自我介绍自我介绍2000美国密苏里州立大学生物化学系博士美国密苏里州立大学生物化学系博士20002003美国休斯敦美国休斯敦Lexicon制药公司高级生物信息学科学家制药公司高级生物信息学科学家2004度中科院百人计划入学者，目前研究方向包括：度中科院百人计划入学者，目前研究方向包括： 1、精子发生、精子发生 2、干细胞自我更新与分化、干细胞自我更新与分化技术专长：分子生物学、干细胞、生物信息学技术专长：分子生物学、干细胞、生物信息学课程描述课程编号：课程编号：511012Y课程属性：课程属性：学科基础课学时学时/学分：学分：40/2预修课程：预修课程：分子生物学、遗传学、统计学、C语言教学目

2、的和要求：教学目的和要求：生物信息学是利用数学模型和计算机程序对生物学研究中产生的数据进行分析计算并得出结论和产生新的科学假说的一种科研手段。通过本课程的教授，使得学生能够：懂得生物学中有哪些数学问题，数学模型和数学手段；利用数据库技术、计算机编程和网页工具来进行基本的生物信息学分析；掌握核酸和蛋白质序列分析的基本技能；懂得如何从芯片和其他高通量技术产生的数据来构建基因调控网络；本课程的开设要求学生有分子生物学、遗传学、统计学及C语言的基础知识和技能，更重要的是要求学生要努力培养自己利用数学模型和逻辑思维来思考和解决生物学问题。本课程为生物学各专业博士、硕士研究生的学科基础课，同时也可作为数理

3、、计算机等相关学科研究生的选修课。本课程的考核方式为大作业和期末考试，比例为50%：50%。 教学大纲第一章第一章 生物信息学入门生物信息学入门 （9学时）学时）1. 生物学中的数学问题生物学中的数学问题(computational problems in biology)（3学时，学时， 3月月2日）日）第二章第二章 序列和结构序列和结构 （15学时）学时）1. 序列比对（序列比对（sequence alignment）（）（3学时，学时，3月月9日）日）第一章第一章 生物信息学入门生物信息学入门 （9学时）学时）2. 数据库原理、数据库原理、PHP编程入门（编程入门（3学时：学时：3学时上

4、机，学时上机，3月月16日）日）3. R语言和语言和Bioconductor软件包（软件包（3学时：学时：3学时上机，学时上机， 3月月23日）日）第二章第二章 序列和结构序列和结构 （15学时）学时）2. 进化树进化树(phylogenetic trees)（1.5学时，学时，3月月30日）日）3。模式发现（。模式发现（motif discovery）（）（1.5学时，学时，3月月30日）日）4. RNA二级结构（二级结构（RNA secondary structure）(3学时，学时，4月月6日，王秀杰日，王秀杰)5. 蛋白质结构分析（蛋白质结构分析（protein structure a

5、nalysis）（）（6学时，学时，4月月13日，蒋太交）日，蒋太交）第三章第三章 从芯片数据到基因调控网络从芯片数据到基因调控网络 （15）3.1 生物芯片设计生物芯片设计(microarray design)（1学时学时, 4月月27日）日）3.2 表达值计算表达值计算(summation of expression value)（1学时学时, 4月月27日）日）3.3 归一化归一化(normalization)（1学时学时, 4月月27日）日）3.4 差异基因的分析差异基因的分析(differential gene expression)（3学时学时, 5月月4日）日）3.5 聚类分析聚

6、类分析(clustering)（3学时学时, 5月月11日）日）3.6 网络入门网络入门(introduction to networks)（3学时学时, 5月月18日）日）3.7 贝叶斯网络等贝叶斯网络等(Basian networks and others)（3学时学时, 5月月25日）王秀杰）日）王秀杰）参考书教材：教材： 本课程以科研文献阅读为主，没有特定教材。主要参考书：主要参考书：1. 简明生物信息学 钟扬， 张亮，赵琼主编 高等教育出版社 20012. 常用生物数据分析软件 王俊，丛丽娟，郑洪坤著 科学出版社 20083. Bioinformatics: sequence and

7、 genome analysisDavid W. MountNew York : Cold Spring Harbor Laboratory, 2004Features of my lecturesEnlightening启发式Interactive互动式Interesting有趣的English（半）英语的第一章 生物信息学入门 1. 生物学中的数学问题(computational problems in biology) Outlines1. What is bioinformatics?2. Basic knowledges3. Mathematical problems in biol

8、ogical researches: From Mendel to nowadays!Bioinformaticswhat is it? What is a triangle? What is human beings? Platos definition What is bioinformatics? BiologysubjectComputer-toolMathematicsModel It is what you are doing for solving biological problems using a computer !Bioinformatics in the Univer

9、seUniverse (宇宙宇宙=空间空间+时间时间)Human civilizationsciencesartsreligionsNatural sciencesSocial sciencesbiologymathematicsphysicsbiostatisticsbioinformaticsComputational biologyNon-human worldWhat do you mean by biology? Taxonomy Physiology Evolution Cell biology Genetics Molecular biology-DNA, RNA, Protei

10、nHow about computer? yesnoPC, ServerQuantum computer, DNA computerInternetTCP/IPWebsiteElectronic businessFTPP2PTelnetHackerPCAppleUnix/LinuxChinese versionC, Perl, PHP, JAVA, .NETcompilerDatabaseSpread sheetAnd mathematics?Object（Subject）：Mathematics is the study of quantity （arithmetic，算术）, struct

11、ure （algebra, 代数）, space （geometry，几何）, and change （calculus , 微积分）.Pure mathmatics vs Applied mathematicsGoldbach Conjecture vs StatisticsDefinition, axiom, statementReasoning (proof)theorem (truth, knowledge) How does does mathematics work?Outlines1. What is bioinformatics?2. Basic knowledges3. Ma

12、thematical problems in biological researches: From Mendel to nowadays!Definitions, notions, terminologySets A set is a group of objects. Elements/members A=7, 21, 57 7A,8Objects, classes, interactionsLaws of Thought 1.Law of identity: Whatever is, is. 2.Law of noncontradiction: Nothing can both be a

13、nd not be. 3.Law of excluded middle: Everything must either be or not be.Reasoning, Logic, Argument Reasoning is the cognitive process of looking for reasons, beliefs, conclusions, actions or feelings.Logic is the study of reasoning.An argument is a set of one or more meaningful declarative sentence

14、s (or propositions) known as the premises along with another meaningful declarative sentence (or proposition) known as the conclusion. One approach to the study of reasoning is to identify various forms of reasoning that may be used to support or justify conclusions. The main division between forms

15、of reasoning that is made in philosophy is between deductive reasoning and inductive reasoning. Formal logic has been described as the science of deduction. The study of inductive reasoning is generally carried out within the field known as informal logic or critical thinking.Deductive reasoning Pre

16、mise 1: All humans are mortal. Premise 2: Socrates is a human. Conclusion: Socrates is mortal.Inductive reasoning Premise: The sun has risen in the east every morning up until now. Conclusion: The sun will also rise in the east tomorrow.Statistical inference Statistical inference is the process of m

17、aking conclusions using data that is subject to random variation, for example, observational errors or sampling variation. Outlines1. What is bioinformatics?2. Basic knowledges3. Mathematical problems in biological researches: From Mendel to nowadays!Biological Story 1Medels LawsMedels Law of Segreg

18、ation The First Law When any individual produces gametes, the copies of a gene separate so that each gamete receives only one copy. Binary phenotypeDominanceGametesStatisticsCombinationMedels Law of Independent AssortmentThe Second LawAlleles of different genes assort independently of one another du

19、ring gamete formation. Computational ProblemsCombinatorial principles组合原理组合原理Rule of sum （加法原理）Rule of product （乘法原理）More about Mendels Laws Gregor Johann Mendel, a 19th century Austrian Priest/monkTrained as physicist and majority of his published works related to meteorology. Between 1856 and 1863

20、, he cultivated and tested some 29,000 pea plants.published in 1866.In 1900，re-discovered by three European scientists, Hugo de Vries, Carl Correns, and Erich von Tschermak. William Bateson, who coined the term genetics, gene, and allele to describe many of its tenets. Very few true Mendelian charac

21、ters in nature.R.A. Fisher Thomas Hunt Morgan, Chromosome, classic geneticsBiological Story 2Hardy-Weinberg LawHardy-Weinberg Law (1908)P(A1) = p, P(A2) = q;Random matingP(A1A1) = p2, P(A1A2) = 2pq, P(A2A2) = q2; Parent generation:P(A1A1) = p11, P(A1A2) = p12, P(A2A2) = p22; p11 + p12 + p22 = 1;P(A1

22、) = p=p11+0.5p12, P(A2) = q=p22+0.5p12; p + q = 1;Matingfrequencyoffspring genotypeFatherMotherA1A1A1A2A2A2A1A1A1A1p11p11100?Thomas Hunt Morgan September 25, 1866 December 4, 1945 American evolutionary biologist, geneticist and embryologist Nobel Prize in Physiology or Medicine in 1933 for discoveri

23、es relating the role the chromosome plays in heredity 22 books and 370 scientific papers The Division of Biology he established at the California Institute of Technology has produced seven Nobel Prize winners.Biological Story 3Linear arrangement of genesFirst Genetic MapAlfred Sturtevant (1891-1970)

24、Undergraduate1 mu = 1 cM = 1% = 0.88 MbpCrossover vs recombinationIsnt there a problem using recombination rate as a measure of distance?Maximum recombination rate (q) is 0.5Distance (l) should be defined as the expected number of crossovers.ABCD q =0.05P0.05H1Type I and Type II errorsType

25、I errorsType II errorsSensitivity & SpecificityBiological Story 5Sequence alignmentDo you think it is easy?A T T C G G C A T T C A G T G C T A G AA T T C G G C A T T G C T A G A!)!(alignments ofnumber totalnmmn Many ways Dot matrix analysis Dynamic programming Word or k-tuple methods (FASTA, BLA

26、ST) S E Q U E N C E A N A L Y S I S P R I M E R S E Q U E N C E A N A L Y S I S P R I M E R Algorithms In mathematics and computer science, an algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function.Phylogeny reconstructionDistance Based Cl

27、ustering, UPGMA (unweighted pair-group method using arithmetic averages)Character Based Maximum parsimony Maximum likelihoodYou will have a good understanding of different algorithms in this field.Story 6Genomics and all other omicsWhere are all the data from? High throughput sequencing EST SAGE mic

28、roarrayGenomics DefinedTraditional Biology:Single gene studyGenomic Biology: Genome wide gene studyKnockout by homologous recombination:From cloning to function study, it takes 23 years in a traditional lab.Knockout by gene trapping:50% of the mouse genes knocked out in ES cells; 1000 mutant mice ar

29、e evaluated every year in Lexicon.Reverse GeneticsGenetics: From function to geneReverse Genetics: From gene to functionFrom disease condition to gene cloning to drug developmentFunction of majority of the 30,000 human genes remains unknown.OverexpressionAnti-sense RNAInterfering RNA (knock down)Mouse gene knockoutHigh Throughput TechnologiesTraditional techs:High throughput tech