图书简介

Concepts in Bioinformatics and Genomics (Detailed Table of Contents); Preface xxx; About the Author xxx; Chapter I: Review of Molecular Biology; Learning outcomes; 1.1 Genes and DNA; 1.2 RNA-the intermediary; 1.3 Amino acids-the building blocks of proteins; 1.4 Levels of protein structure; 1.5 The genetic code; 1.6 Relative sizes of matter; 1.7 DNA alterations; 1.8 A case study: sickle cell anemia; DT What are the symptoms of sickle cell anemia?; DT Sickle cell anemia is the first disease linked to a specific mutation; 1.9 Introduction to p53; Summary; `L Exercises; References; Box 1-1. A Closer Look: A rare inherited cancer is caused by mutated Tp53; Chapter 2: Information organization and sequence databases; Learning outcomes; 2.1 Introduction; 2.2 Public databases; 2.3 The header; 2.4 The feature keys; DT The CDS feature key and gene structure; DT The gene feature key and FASTA format; DT Thought Question 2.1; 2.5 Limitations of GenBank; 2.6 Reference Sequence (RefSeq); DT Alternative splicing; 2.7 Primary and secondary databases; DT The UniProt Knowledge Base (UniProtKB) database; Summary; Exercises; Answers to Thought Questions; References; Box 2-1. Scientist Spotlight: Walter Goad, GenBank Founder; Box 2-2. A Closer Look: GenBank is Critical to the Discovery of the MDM2 Oncoprotein-an Inhibitor of p53; Chapter 3: Molecular Evolution; Learning outcomes; 3.1 Introduction; 3.2 Conserved regions in proteins; 3.3 Molecular Evolution; DT Transformation of normal cells to cancer cells; DT Are mutations inherited?; DT Natural selection; DT Mechanisms of mutation; 3.4 Ancestral genes and protein evolution; 3.5 Modular proteins and protein evolution; Summary; Exercises; References; Box 3-1. Scientist Spotlight: Barbara McClintock; Chapter 4: Substitution matrices; Learning outcomes; 4.1 Introduction; 4.2 The identity substitution matrix; 4.3 An amino acid substitution system based on natural selection; 4.4 Development of the matrix of accepted amino acid substitutions; DT Thought Question 4-1; 4.5 Relative mutability calculations; 4.6 Development of the PAM1 mutation probability matrix; 4.7 Determination of the relative frequencies of amino acids; 4.8 Conversion of the PAM1 mutation probability matrix to the PAM1 log-odds substitution matrix; 4.9 Conversion of the PAM1 mutational probability matrix to other PAM; 4.10 Practical uses for PAM substitution matrices; 4.11 The BLOSUM substitution matrix; DT Thought Question 4-2; 4.12 The physico-chemical properties of amino acids correlate to values in matrices; 4.13 Practical usage; Summary; Exercises; Answers to Thought Questions; References; Box 4-1. Scientist Spotlight: Margaret Belle (Oakley) Dayhoff; Chapter 5: Pairwise sequence alignment; Learning outcomes; 5.1 Introduction; 5.2 Sliding window; DT Dot plots; DT The Dotter program; 5.3 The Needleman-Wunsch global alignment program; DT Initialization and matrix fill; DT Traceback; DT Gap penalties; 5.4 Modified Needleman-Wunsch global alignment (N-Wmod) program with linear gap penalty; DT N-Wmod initialization; DT N-Wmod matrix fill; DT N-Wmod traceback; 5.5 Ends-free global alignment; 5.6 Local alignment algorithm with linear gap penalty; Summary; Exercises; References; Box 5-1. Scientist Spotlight: Christian Wunsch; Chapter 6: Basic Local Alignment Sequence Tool and Multiple Sequence Alignment; Learning outcomes; 6.1 Introduction; 6.2 The BLAST program; DT Four phases in the BLAST program; DT How does BLAST account for gaps?; DT How is a hit deemed to be statistically significant?; DT Thought Question 6-1; DT Why is the BLAST program faster than the Smith-Waterman program?; DT Low complexity regions and masking; DT Usefulness of BLAST; DT Psi-BLAST; DT Thought Question 6-2; 6.3. Multiple Sequence Alignment (MSA); DT CLUSTALW; Summary; Exercises; Answers to Thought Questions; References; Box 6-1. Scientist Spotlight: David Lipman, NCBI Director; Chapter 7: Protein structure prediction; Learning outcomes; 7.1 Introduction; 7.2 Experimental methods of structure determination; DT X-ray crystallography; DT NMR spectroscopy; 7.3 Information deposited into the Protein Data Bank; 7.4 Molecular viewers; DT Thought question 7-1; 7.5 Protein folding; DT Christian Anfisen’s protein unfolding and refolding experiment; DT Local minimum energy states; DT Energy Landscape theory; 7.6 Protein structure prediction methods; DT Prediction method 1: computational methods; DT Combining computational methods and knowledge-based systems; DT Calculation of accuracy of structure predictions; DT Prediction method 2: statistical and knowledge-based methods; DT Prediction method 3: neural networks; DT Prediction method 4: homology modeling; DT Prediction method 5: Threading; Summary; Exercises; Answers to Thought Questions; References; Box 7-1. A Closer Look: p53 co-crystallized with DNA reveals insights into cancer; Chapter 8: Phylogenetics; Learning outcomes; 8.1 Introduction; 8.2 Phylogeny and phylogenetics; DT Molecular clocks; DT Phylogenetic tree nomenclature; DT How to tell if sequences in two lineages are undergoing sequence substitution at nearly equal rates?; DT DNA, RNA and protein-based trees; 8.3 Two classes of tree-generation methods; DT Unweighted pair group method with arithmetic mean (UPGMA); DT Thought question 8-1; DT Thought question 8-2; DT Thought question 8-3; DT Thought question 8-4; DT Bootstrap analysis; DT Other substitution rate models-Kimura two-parameter model and Gamma distance model; DT Neighbor-Joining method; 8.4 Application of phylogenetics to studies of the origin of modern humans; 8.5 Phylogenetic Tree of Life; 8.6 The Tp53 gene family members in different species; Summary; Exercises; Answers to Thought Questions; References; Box 8-1. A Closer Look: What do we know about Neanderthal and Denisovan?; Box 8-2. Scientist Spotlight: Svante Paabo; Chapter 9. Genomics; Learning outcomes; 9.1 Introduction; 9.2 DNA sequencing-dideoxy method; DT Dideoxy nucleotides; DT The step-by-step procedure of DNA sequencing; DT Electrophoresis; DT Thought question 9-1; 9.3 Polymerase chain reaction (PCR); 9.4 DNA sequencing-next generation (next-gen) sequencing technologies; DT Common themes in next-gen sequencing technologies; DT Ion semiconductor sequencing; DT Nanoport-based sequencing; 9.5 The PhiX174 bacteriophage genome; 9.6 The genome of Haemophilus influenzae Rd. and the whole genome shotgun sequencing approach; DT The whole genome shotgun approach; DT Thought question 9-2; DT The Haemophilus influenzae Rd. genome; 9.7 Genome assembly and annotation; DT Contig N50 and scaffold N50; DT Bacterial genome annotation systems; 9.8 Genome comparisons; DT Synteny Dotplot; DT Comparison of E. coli Substrain DH10B to E. coli Substrain MG1655; 9.10 The human genome; DT General characteristics of the human genome; DT Thought question 3; DT Detailed analysis of the human genome landscape; 9.11 The region of the human genome that encompasses the Tp53 gene; DT General comments on the region encoding the Tp53 gene; DT Tracks that display information about the Tp53 region of the genome; 9.12 The haplotype map; DT What is a haplotype?; DT Haplotypes can be specified by markers derived from SNPs, indels and CNVs; DT Tag SNPs; DT Thought question 9-4; DT How did haplotypes originate?; DT The HapMap database; 9.13 Practical application of Tag SNP, SNP and mutation analyses; 9.14 What is the smallest genome?; Summary; Exercises; Answers to Thought Questions; References; Box 9-1. Scientist Spotlight: J. Craig Venter; Box 9-2. A Closer Look: DNA Fingerprinting (DNA Profiling); Chapter 10. Transcript and protein expression analysis; Learning outcomes; 10.1 Introduction; 10.2 Basic principles of gene expression; 10.3 Measurement of transcript levels; DT Thought question 10-1; 10.4 The transcriptome and microarrays; DT Stages of a microarray experiment; DT Heatmaps; DT Thought question 10-2; DT Cluster analysis; DT Thought question 3; DT Practical applications of microarray data; DT Considerations to take in the interpretation of microarray data; DT Protein levels can be controlled by regulation of degradation rate; 10.5 RNA-seq (RNA sequencing); DT Advantages of RNA-seq; DT Overview of RNA-seq steps; DT Bridge amplification; DT Analysis of an experiment using RNA-seq; 10.6 Proteome; DT Separation of proteins and quantification of their steady-state levels-two-dimensional (2D) gel electrophoresis; DT Identification of proteins-liquid chromatography-mass spectroscopy (LC-MS); DT Advantages and challenges of current proteome analysis techniques; 10.7 Regulation of p53-controlled genes; Summary; Exercises; Answers to Thought Questions; References; Box 10-1. Scientist Spotlight: Patrick O. Brown; Chapter 11. Basic probability; Learning outcomes; 11.1 Introduction; 11. 2 The basics of probability; DT Definitions and basic rules; DT Counting methods when order matters; DT Counting methods when order does not matter; DT Independence; DT Dependence; DT Thought Question 11-1; DT Bayesian inference; DT Thought Question 11-2; 11.3 Random variables; DT Discrete random variables; DT Thought Question 11-3; DT Thought Question 11-4; DT Continuous random variables; Summary; Exercises; Answers to Thought Questions; References; Chapter 12. Advanced probability for bioinformatics applications; Learning outcomes; 12.1 Introduction; 12.2 Extreme value distribution; 12.3 Significance of alignments; 12.4 Stochastic processes; DT Markov chains; DT Thought Question 12-1; DT Hidden Markov models; DT Poisson process and Jukes-Cantor Model; Summary; Exercises; Answers to Thought Questions; References; Box 12-1 Scientist Spotlight: Michael Waterman; Chapter 13. Programming basics and applications to bioinformatics; Learning outcomes; 13.1 Introduction; 13.2 Developers and users work together to make new discoveries.; 13.3 Why Python?; 13.4 Getting started with Python; 13.5 Data flow: representing and manipulating data; DT Variable names; DT Data types and operators; 13.6 Putting it together-a simple program to lookup the hydrophobicity of an amino acid; 13.7 Decision making; DT Operations for decision making; DT If-tests; DT Conditional expressions; DT Loops; DT Thought Question 13-1; DT Thought Question 13-2; DT Thought Question 13-3; 13.8 Input and output; 13.9 Program design: developing Kyte-Doolittle’s hydropathy sliding window tool; DT Step 1: Understand the problem; DT Steps 2 through 4: Develop and refine algorithm; DT Step 5: Code in target language (Python); DT Steps 6 and 7: Program verification (testing and debugging); DT Thought Question 13-4; 13.10 Hierarchical design: functions and modules; DT Python functions; DT Thought Question 13.5; DT Python modules and packages; Summary; Exercises; Answers to Thought Questions; References; Box 13-1. Scientist Spotlight: Russell F. Doolittle; Chapter 14. Developing a bioinformatics tool; Learning outcomes; 14.1 Introduction; 14.2 Analysis of an existing tool: EMBOSS water local alignment tool; DT Thought question; 14.3 Overview of SPA: A simple pairwise alignment tool; 14.4 Algorithms; 14.5 Algorithms for SPA; DT Input sequences; DT Create substitution matrix; DT Input gap penalties; DT Suite of pairwise sequence alignment algorithms; DT Output alignment; 14.6 Algorithm complexity; 14.7 Extensions to simple pairwise alignment tool; Summary; Exercises; Project; Answers to Thought Questions; References; I. Box 14-1. Scientist Spotlight: Richard Karp; Glossary; Index

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