《生物信息學課程導引(英文版)》根據清華大學承辦的全國生物信息學暑期學校課程，高度概括地介紹了與生物信息學研究緊密相關的11門基礎課程和15個前沿專題報告。全書分12章，包括生物信息學引論、生物信息學中的基礎統計、計算基因組學專題、生物信息學中的高級統計、計算生物學算法基礎、生物信息學中的多元統計、人類疾病關聯研究方法與實例、生物信息學中的數據挖掘與知識發現、生物信息學套用工具、蛋白質結構與功能基礎、中醫藥研究的計算系統生物學方法、生物信息學與計算系統生物學前沿等。
基本介紹
- 中文名：生物信息學課程導引
- 作者：江瑞、張學工、張奇偉
- 出版時間：2014年5月1日
- 出版社：清華大學出版社
- 頁數：395 頁
- ISBN：9787302323594
- 定價：99 元
- 開本：16 開
內容簡介,圖書目錄,作者簡介,
內容簡介
《生物信息學課程導引(英文版)》不僅可以作為生物信息學初學者的入門讀物，還可作為生物信息學領域專業研究人員高度概括而又不失系統性的參考書籍。
圖書目錄
1 Basics for Bioinformatics
1.1 What Is Bioinformatics
1.2 Some Basic Biology
1.2.1 Scale and Time
1.2.2 Cells
1.2.3 DNA and Chromosome
1.2.4 The Central Dogma
1.2.5 Genes and the Genome
1.2.6 Measurements Along the Central Dogma
1.2.7 DNA Sequencing
1.2.8 Transcriptomics and DNA Microarrays
1.2.9 Proteomics and Mass Spectrometry
1.2.10 ChIP—Chip and ChIP—Seq
1.3 Example Topics of Bioinformatics
1.3.1 Examples of Algorithmatic Topics
1.3.2 Examples of Statistical Topics
1.3.3 Machine Learning and PatternRecognition Examples
1.3.4 Basic Principles of Genetics
References
2 Basic Statistics for Bioinformatics
2.1 Introduction
2.2 Foundations of Statistics
2.2.1 Probabilities
2.2.2 Random Variables
2.2.3 Multiple Random Variables
2.2.4 Distributions
2.2.5 Random Sampling
2.2.6 Sufficient Stafistics
2.3 Point Estimation
2.3.1 Method of Moments
2.3.2 Maximum Likelihood Estimators
2.3.3 Bayes Esfimators
2.3.4 Mean Squared Error
2.4 Hypothesis Testing
2.4.1 Likelihood Ratio Tests
2.4.2 Error Probabilities and the Power Function
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
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 Transcriptional Genomics Data
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—Scoring Sites
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
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 Identification
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 Gibbs Motif Sampler
4.5.3 Phylogenetic Footprinting Method and the Identification of Cis—Regulatory Modules
4.6 Combining Sequence and Expression Information for Analyzing Transcription Regulation
4.6.1 MotifDiscoveryinChIP—ArrayExperiment
4.6.2 Regression Analysis of TranscriptionRegulation
4.6.3 Regulatory Role of Histone Modification
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
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 Approximation Algorithm
References
6 Multivariate Statistical Methods in Bioinformatics Research
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 Classification 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 Example
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
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
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，and Function Databases
9.11.6 Structure Databases
9.12 BioinformaticsTools
9.12.1 List of Bioinformatics Tools at International Bioinformatics Centers
9.12.2 Web—BasedBioinformaticsPlatforms
9.12.3 Bioinformatics Packages to be Downloaded and Installed Locally
9.13 Sequence Analysi
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 Identification
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 Visualizationand Modeling Tools
9.15.2 Protein Modeling Web Servers
9.16 Phylogenetic Analysis and Tree Construction
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
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—Dimensional Structure of Protein
10.3 Fundamentalof MacromoleculesStructuresand Functions
10.3.1 Different Levelsof Protein Structure
10.3.2 Primary Structure
10.3.3 Secondary Structure
10.3.4 Supersecondary Structure
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 AbInitio Prediction Method
Reference
11 Computational Systems Biology Approaches for Deciphering Traditional Chinese Medicine
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 Phenotype Network
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
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 Profile of Transmembrane Gene Families
12.4 Evolution of Combinatorial Transcriptional Circuits inthe Fungal Lineage
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 Identicationof Network Motifs in Random Networks
12.13 Examples of Pattern Recognition Applicationsin Bioinformatics
12.14 Considerationsin Bioinformatics
作者簡介
作者:江瑞(Rui Jiang)、張學工(Xuegong Zhang)、張奇偉(Michael Q. Zhang)
