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　　本书根据清华大学承办的全国生物信息学暑期学校课程，高度概括地介绍了与生物信息学研究紧密相关的11门基础课程和15个前沿专题报告。全书分12章，包括： 生物信息学引论、生物信息学中的基础统计、计算基因组学专题、生物信息学中的高级统计、计算生物学算法基础、生物信息学中的多元统计、人类疾病关联研究方法与实例、生物信息学中的数据挖掘与知识发现、生物信息学应用工具、蛋白质结构与功能基础、中医药研究的计算系统生物学方法、生物信息学与计算系统生物学前沿等。本书不仅可以作为生物信息学初学者的入门读物，还可作为生物信息学领域专业研究人员高度概括而又不失系统性的参考书籍。

1  BasicsforBioinfbrmatics.

  Xuegong Zhang，Xueya Zhou，and Xiaowo Wang

  1.1    WhatIs l3；ioinformatics

  1.2  SomeBasicBiology

    1.2.1  Scale andTime.

    1.2.2  Cells.

    1.2.3  DNA and Chromosome

    1.2.4  TheCen~a1Dogma.

    1.2.5  GenesandtheGenome.一

    1.2.6  Measurements Along the Central Dogma

    1.2.7  DNA Sequencing一

    1.2.8  Transcriptomics and DNA Microarrays

    1.2.9  Proteomics and Mass Spectrometry.

    1.2.10  ChIP-Chip andChIP.Seq

    1.3    ExampleTopicsofBioinformatics

    1.3.1  Examples of Algorithmatic Topics

    1.3.2  ExamplesofStatisticalTopics.

    1.3.3  Machine Learning and Pattern

    RecognitionExamples

    1.3.4  Basic Principles ofGenetics.

    Re：fe：rences

2  Basic StatisticsforBioinformatics.

    Yuanlie Lin and Rui Jiang

  2.1    Introduction.

  2.2    FoundationsofStatistics

    2.2.1  Probabilities

    2.2.2  RandomVariables

    2.2.3  Multiple Random Variables

    2.2.4  Distributions.

2.2.5 random sampling. 

2.2.6 suf.cientstatistics 

2.3 point estimation  

2.3.1 method of moments. 

2.3.2 maximum likelihoodestimators  

2.3.3 bayes estimators 

2.3.4 mean squared error. 

2.4 hypothesistesting 

2.4.1 likelihood ratio tests  

2.4.2 errorprobabilitiesandthepowerfunction 

2.4.3 p-values 

2.4.4 some widely used tests

2.5 intervalestimation 

2.6 analysis of variance 

2.6.1 one-way analysis of variance. 

2.6.2 two-wayanalysisofvariance. 

2.7 regression models 

2.7.1 simple linear regression. 

2.7.2 logistic regression

2.8 statisticalcomputingenvironments. 

2.8.1 downloadingand installation  

2.8.2 storage, input, and outputof data. 

2.8.3 distributions. 

2.8.4 hypothesis testing  

2.8.5 anova and linear model

references 

3 topics in computational genomics  69 michael q. zhang and andrew d. smith

3.1 overview:genomeinformatics 

3.2 finding protein-codinggenes. 

3.2.1 how to identifya coding exon

3.2.2 how to identifya gene with multiple exons  

3.3 identifyingpromoters. 

3.4 genomic arraysand acgh/cnp analysis 

3.5 introduction on computational analysis of transcriptionalgenomicsdata

3.6 modelingregulatory elements  

3.6.1 word-based representations

3.6.2 thematrix-basedrepresentation 

3.6.3 other representations. 

3.7 predicting transcriptionfactor binding sites. 

3.7.1 the multinomial model for describing sequences

3.7.2 scoring matrices and searching sequences

3.7.3 algorithmic techniques for identifying high-scoringsites  

3.7.4 measuring statistical signi.cance of matches  

3.8 modelingmotif enrichmentin sequences  

3.8.1 motif enrichmentbased on likelihoodmodels. 

3.8.2 relative enrichment between two sequence sets  

3.9 phylogeneticconservationof regulatoryelements  

3.9.1 three strategies for identifying conserved binding sites  

3.9.2 considerationswhen using phylogeneticfootprinting 

3.10 motif discovery. 

3.10.1 word-basedandenumerativemethods 

3.10.2 general statistical algorithms applied to motif discovery  

3.10.3 expectationmaximization 

3.10.4 gibbs sampling

references 

4 statistical methods in bioinformatics  101 jun s. liu and bo jiang

4.1 introduction

4.2 basics of statistical modeling and bayesian inference. 

4.2.1 bayesian method with examples. 

4.2.2 dynamic programmingand hidden markovmodel  

4.2.3 metropolis-hastingsalgorithm and gibbs sampling  

4.3 gene expressionand microarrayanalysis 

4.3.1 low-level processing and differential expression identi.cation 

4.3.2 unsupervised learning

4.3.3 dimensionreductiontechniques 

4.3.4 supervised learning  

4.4 sequencealignment 

4.4.1 pair-wise sequence analysis. 

4.4.2 multiple sequence alignment 

4.5 sequence pattern discovery 

4.5.1 basic models and approaches 

4.5.2 gibbsmotifsampler 

4.5.3 phylogenetic footprinting method and the identi.cation of cis-regulatorymodules. 

4.6 combining sequence and expression information for analyzing transcriptionregulation

4.6.1 motifdiscoveryinchip-arrayexperiment. 

4.6.2 regression analysis of transcriptionregulation

4.6.3 regulatoryroleofhistonemodi.cation 

4.7 protein structure and proteomics  

4.7.1 protein structure prediction  

4.7.2 protein chip data analysis. 

references 

5 algorithms in computational biology . 151 tao jiang and jianxing feng

5.1 introduction

5.2 dynamic programmingand sequence alignment  

5.2.1 the paradigm of dynamic programming 

5.2.2 sequence alignment  

5.3 greedy algorithmsfor genome rearrangement 

5.3.1 genome rearrangements

5.3.2 breakpoint graph, greedy algorithm and approximationalgorithm  159 references 

6 multivariate statistical methods in bioinformatics research . 163 lingsongzhang and xihong lin

6.1 introduction

6.2 multivariate normal distribution  

6.2.1 de.nition and notation

6.2.2 properties of the multivariate normal distribution

6.2.3 bivariate normal distribution  

6.2.4 wishart distribution. 

6.2.5 sample mean and covariance

6.3 one-sampleand two-sample multivariate hypothesis tests 

6.3.1 one-sample t test for a univariate outcome

6.3.2 hotelling's t2 test for the multivariate outcome  

6.3.3 properties of hotelling'st2 test. 

6.3.4 paired multivariate hotelling's t2 test 

6.3.5 examples  

6.3.6 two-samplehotelling's t2 test

6.4 principalcomponentanalysis. 

6.4.1 de.nition of principal components 

6.4.2 computing principalcomponents

6.4.3 variance decomposition 

6.4.4 pcawithacorrelationmatrix. 

6.4.5 geometricinterpretation 

6.4.6 choosing the numberof principal components  

6.4.7 diabetes microarraydata. 

6.5 factor analysis 

6.5.1 orthogonalfactor model

6.5.2 estimating the parameters  

6.5.3 an example

6.6 linear discriminant analysis  

6.6.1 two-grouplinear discriminant analysis. 

6.6.2 an example

6.7 classi.cation methods

6.7.1 introductionof classi.cation methods  

6.7.2 k-nearestneighbormethod 

6.7.3 density-basedclassi.cationdecisionrule. 

6.7.4 quadraticdiscriminantanalysis. 

6.7.5 logistic regression

6.7.6 supportvector machine  

6.8 variableselection. 

6.8.1 linear regression model

6.8.2 motivation for variable selection  

6.8.3 traditionalvariableselectionmethods 

6.8.4 regularization and variable selection

6.8.5 summary  

references 

7 association analysis for human diseases: methods and examples . 233 jurg ott and qingrunzhang

7.1 whydoweneedstatistics. 

7.2 basic concepts in population and quantitative genetics. 

7.3 genetic linkageanalysis  

7.4 geneticcase-controlassociationanalysis. 

7.4.1 basic steps in an association study

7.4.2 multiple testing corrections

7.4.3 multi-locusapproaches  

7.5 discussion. 

references 

8 data mining and knowledge discovery methods with case examples  

s. bandyopadphyayand u. maulik

8.1 introduction

8.2 different tasks in data mining  

8.2.1 classi.cation  

8.2.2 clustering 

8.2.3 discoveringassociations. 

8.2.4 issues and challengesin data mining

8.3 some commontools and techniques. 

8.3.1 arti.cial neural networks 

8.3.2 fuzzy sets and fuzzy logic 

8.3.3 genetic algorithms

8.4 case examples 

8.4.1 pixelclassi.cation 

8.4.2 clustering of satellite images  

8.5 discussionandconclusions 

references 

9 applied bioinformatics tools 271 jingchu luo

9.1 introduction

9.1.1 welcome. 

9.1.2 about this web site  

9.1.3 outline

9.1.4 lectures 

9.1.5 exercises. 

9.2 entrez 

9.2.1 pubmed query  

9.2.2 entrez query  

9.2.3 my ncbi  

9.3 expasy 

9.3.1 swiss-prot query 

9.3.2 explore the swiss-prot entry hba human. 

9.3.3 database query with the ebi srs

9.4 sequencealignment 

9.4.1 pairwise sequence alignment 

9.4.2 multiple sequence alignment 

9.4.3 blast  

9.5 dna sequence analysis

9.5.1 gene structure analysis and prediction

9.5.2 sequencecomposition 

9.5.3 secondarystructure. 

9.6 protein sequence analysis

9.6.1 primary structure 

9.6.2 secondarystructure. 

9.6.3 transmembranehelices  

9.6.4 helical wheel

9.7 motif search  

9.7.1 smart search 

9.7.2 memesearch. 

9.7.3 hmm search  

9.7.4 sequence logo  

9.8 phylogeny

9.8.1 protein

9.8.2 dna

9.9 projects  

9.9.1 sequence, structure, and function analysis of the bar-headed goose hemoglobin. 

9.9.2 exercises. 

9.10 literature  

9.10.1 courses and tutorials

9.10.2 scienti.c stories  

9.10.3 free journalsand books 

9.11 bioinformaticsdatabases  

9.11.1 list of databases  

9.11.2 database query systems 

9.11.3 genome databases 

9.11.4 sequencedatabases. 

9.11.5 proteindomain,family,andfunctiondatabases. 

9.11.6 structure databases

9.12 bioinformaticstools 

9.12.1 list of bioinformatics tools at international bioinformaticscenters 

9.12.2 web-basedbioinformaticsplatforms 

9.12.3 bioinformatics packages to be downloaded and installed locally 

9.13 sequence analysis 

9.13.1 dotplot. 

9.13.2 pairwise sequence alignment 

9.13.3 multiple sequence alignment 

9.13.4 motif finding 

9.13.5 gene identi.cation 

9.13.6 sequence logo  

9.13.7 rna secondary structure prediction  

9.14 database search. 

9.14.1 blast search  

9.14.2 other database search 

9.15 molecular modeling  

9.15.1 visualizationandmodelingtools 

9.15.2 protein modelingweb servers

9.16 phylogeneticanalysisandtreeconstruction. 

9.16.1 list of phylogenyprograms  

9.16.2 online phylogenyservers  

9.16.3 phylogenyprograms  

9.16.4 displayofphylogenetictrees 

references 

10 foundations for the study of structure and function of proteins  303 zhirongsun

10.1 introduction

10.1.1 importanceof protein. 

10.1.2 amino acids, peptides, and proteins. 

10.1.3 some noticeable problems

10.2 basic concept of protein structure  

10.2.1 different levels of protein structures 

10.2.2 acting force to sustain and stabilize the high-dimensionalstructure of protein  

10.3 fundamentalof macromoleculesstructuresand functions  

10.3.1 differentlevelsofproteinstructure. 

10.3.2 primary structure 

10.3.3 secondarystructure. 

10.3.4 supersecondarystructure. 

10.3.5 folds

10.3.6 summary  

10.4 basis of protein structure and function prediction

10.4.1 overview  

10.4.2 the signi.cance of protein structure prediction 

10.4.3 the field of machine learning. 

10.4.4 homological protein structure prediction method 

10.4.5 abinitiopredictionmethod 

reference. 

11 computational systems biology approaches for deciphering traditional chinese medicine  337 shao li and le lu

11.1 introduction

11.2 disease-related network. 

11.2.1 fromagenelisttopathwayandnetwork 

11.2.2 construction of disease-related network. 

11.2.3 biological network modularity and phenotypenetwork. 

11.3 tcm zheng-related network

11.3.1 "zheng" in tcm 

11.3.2 acsb-basedcasestudyfortcmzheng 

11.4 network-based study for tcm "fu fang" 

11.4.1 systems biology in drug discovery

11.4.2 network-based drug design

11.4.3 progresses in herbal medicine

11.4.4 tcm fu fang (herbal formula)

11.4.5 a network-based case study for tcm fu fang

references 

12 advanced topics in bioinformatics and computational biology . 369 bailin hao, chunting zhang, yixue li, hao li, liping wei, minoru kanehisa, luhualai, runsheng chen, nikolaus rajewsky, michael q. zhang, jingdonghan, rui jiang, xuegong zhang, and yanda li

12.1 prokaryotephylogenymeets taxonomy  

12.2 z-curve method and its applications in analyzing eukaryoticand prokaryotic genomes

12.3 insights into the coupling of duplication events and macroevolution from an age pro.le of transmembranegene families

12.4 evolution of combinatorial transcriptional circuits inthefungallineage. 

12.5 can a non-synonymous single-nucleotide polymorphism (nssnp) affect protein function analysis from sequence, structure, and enzymatic assay

12.6 bioinformatics methods to integrate genomic andchemicalinformation 

12.7 from structure-based to system-based drug design  

12.8 progressin the study of noncodingrnas in c. elegans

12.9 identifyingmicrornas and their targets  

12.10 topics in computationalepigenomics  

12.11 understanding biological functions through molecular networks  

12.12 identi.cationof network motifs in random networks  

12.13 examples of pattern recognition applicationsin bioinformatics. 

12.14 considerationsin bioinformatics  

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