Concepts in Bioinformatics and Genomics (Detailed Table of Contents)
Preface xxx
About the Author
1. 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
· What are the symptoms of sickle cell anemia?
· Sickle cell anemia is the first disease linked to a specific mutation
1.9 Introduction to
p53
Summary
Exercises
References
Box 1-1. A Closer Look: A rare inherited cancer is caused by mutated Tp53
2. Information organization and sequence databases
Learning outcomes
2.1 Introduction
2.2 Public databases
2.3 The header
2.4 The feature keys
·
The CDS feature key and gene structure
· The gene feature key and FASTA format
· Thought Question 2.1
2.5 Limitations of GenBank
2.6 Reference Sequence (RefSeq)
· Alternative splicing
2.7 Primary and secondary databases
· 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
3. Molecular Evolution
Learning
outcomes
3.1 Introduction
3.2 Conserved regions in proteins
3.3 Molecular Evolution
· Transformation of normal cells to cancer cells
· Are mutations inherited?
· Natural selection
· 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
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
· 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
· 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
5. Pairwise sequence alignment
Learning
outcomes
5.1 Introduction
5.2 Sliding window
· Dot plots
· The Dotter program
5.3 The Needleman-Wunsch global alignment program
· Initialization and matrix fill
· Traceback
· Gap penalties
5.4 Modified Needleman-Wunsch global alignment (N-Wmod) program with linear
gap penalty
· N-Wmod initialization
· N-Wmod matrix fill
· 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
6. Basic Local
Alignment Sequence Tool and Multiple Sequence Alignment
Learning outcomes
6.1 Introduction
6.2 The BLAST program
· Four phases in the BLAST program
· How does BLAST account for gaps?
· How is a hit deemed to be statistically significant?
· Thought Question 6-1
· Why
is the BLAST program faster than the Smith-Waterman program?
· Low complexity regions and masking
· Usefulness of BLAST
· Psi-BLAST
· Thought Question 6-2
6.3. Multiple Sequence Alignment (MSA)
· CLUSTALW
Summary
Exercises
Answers to Thought
Questions
References
Box 6-1. Scientist Spotlight: David Lipman, NCBI Director
7. Protein structure prediction
Learning outcomes
7.1 Introduction
7.2 Experimental methods of structure determination
· X-ray crystallography
· NMR spectroscopy
7.3 Information
deposited into the Protein Data Bank
7.4 Molecular viewers
· Thought question 7-1
7.5 Protein folding
· Christian Anfisen's protein unfolding and refolding experiment
· Local minimum energy states
· Energy Landscape theory
7.6 Protein structure prediction methods
·
Prediction method 1: computational methods
· Combining computational methods and knowledge-based systems
· Calculation of accuracy of structure predictions
· Prediction method 2: statistical and knowledge-based methods
· Prediction method 3: neural networks
· Prediction method 4:
homology modeling
· 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
8. Phylogenetics
Learning outcomes
8.1 Introduction
8.2 Phylogeny and
phylogenetics
· Molecular clocks
· Phylogenetic tree nomenclature
· How to tell if sequences in two lineages are undergoing sequence substitution at nearly equal rates?
· DNA, RNA and protein-based trees
8.3 Two classes of tree-generation methods
· Unweighted pair group
method with arithmetic mean (UPGMA)
· Thought question 8-1
· Thought question 8-2
· Thought question 8-3
· Thought question 8-4
· Bootstrap analysis
· Other substitution rate models-Kimura two-parameter model and Gamma distance model
· 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 Pääbo
9. Genomics
Learning outcomes
9.1 Introduction
9.2 DNA sequencing-dideoxy method
· Dideoxy nucleotides
· The step-by-step procedure of DNA sequencing
· Electrophoresis
· Thought question 9-1
9.3
Polymerase chain reaction (PCR)
9.4 DNA sequencing-next generation (next-gen) sequencing technologies
· Common themes in next-gen sequencing technologies
· Ion semiconductor sequencing
· Nanoport-based sequencing
9.5 The PhiX174 bacteriophage genome
9.6 The genome of
Haemophilus influenzae Rd. and the whole genome shotgun sequencing approach
· The whole genome shotgun approach
· Thought question 9-2
· The Haemophilus influenzae Rd. genome
9.7 Genome assembly and annotation
· Contig N50 and scaffold N50
· Bacterial genome annotation
systems
9.8 Genome comparisons
· Synteny Dotplot
· Comparison of E. coli Substrain DH10B to E. coli Substrain MG1655
9.10 The human genome
· General characteristics of the human genome
· Thought question 3
· Detailed analysis of the human genome landscape
9.11 The
region of the human genome that encompasses the Tp53 gene
· General comments on the region encoding the Tp53 gene
· Tracks that display information about the Tp53 region of the genome
9.12 The haplotype map
· What is a haplotype?
· Haplotypes can be specified by markers derived
from SNPs, indels and CNVs
· Tag SNPs
· Thought question 9-4
· How did haplotypes originate?
· 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)
10. Transcript and protein expression analysis
Learning outcomes
10.1 Introduction
10.2 Basic principles of gene expression
10.3 Measurement
of transcript levels
· Thought question 10-1
10.4 The transcriptome and microarrays
· Stages of a microarray experiment
· Heatmaps
· Thought question 10-2
· Cluster analysis
· Thought question 3
· Practical applications of microarray data
· Considerations to take
in the interpretation of microarray data
· Protein levels can be controlled by regulation of degradation rate
10.5 RNA-seq (RNA sequencing)
· Advantages of RNA-seq
· Overview of RNA-seq steps
· Bridge amplification
· Analysis of an experiment using RNA-seq
10.6
Proteome
· Separation of proteins and quantification of their steady-state levels-two-dimensional (2D) gel electrophoresis
· Identification of proteins-liquid chromatography-mass spectroscopy (LC-MS)
· 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
11. Basic probability
Learning outcomes
11.1 Introduction
11. 2 The basics of probability
· Definitions and basic rules
·
Counting methods when order matters
· Counting methods when order does not matter
· Independence
· Dependence
· Thought Question 11-1
· Bayesian inference
· Thought Question 11-2
11.3 Random variables
· Discrete random variables
· Thought Question 11-3
·
Thought Question 11-4
· Continuous random variables
Summary
Exercises
Answers to Thought Questions
References
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
· Markov chains
· Thought Question 12-1
· Hidden Markov models
· Poisson process and Jukes-Cantor Model
Summary
Exercises
Answers to Thought Questions
References
Box 12-1 Scientist Spotlight: Michael Waterman
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
· Variable
names
· Data types and operators
13.6 Putting it together-a simple program to lookup the hydrophobicity of an amino acid
13.7 Decision making
· Operations for decision making
· If-tests
· Conditional expressions
· Loops
· Thought Question 13-1
· Thought Question
13-2
· Thought Question 13-3
13.8 Input and output
13.9 Program design: developing Kyte-Doolittle's hydropathy sliding window tool
· Step 1: Understand the problem
· Steps 2 through 4: Develop and refine algorithm
· Step 5: Code in target language (Python)
· Steps 6 and
7: Program verification (testing and debugging)
· Thought Question 13-4
13.10 Hierarchical design: functions and modules
· Python functions
· Thought Question 13.5
· Python modules and packages
Summary
Exercises
Answers to Thought Questions
References
Box
13-1. Scientist Spotlight: Russell F. Doolittle
14. Developing a bioinformatics tool
Learning outcomes
14.1 Introduction
14.2 Analysis of an existing tool: EMBOSS water local alignment tool
· Thought question
14.3 Overview of SPA: A simple pairwise alignment tool
14.4
Algorithms
14.5 Algorithms for SPA
· Input sequences
· Create substitution matrix
· Input gap penalties
· Suite of pairwise sequence alignment algorithms
· 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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Jamil Momand has been a professor of biochemistry at California State University, Los Angeles (Cal State LA) since 1999. Dr. Momand received the Cal State LA Outstanding Professor Award for the 2014-2015 academic year. Alison McCurdy is a Professor of Chemistry at California State University,
Los Angeles. She was the recipient of the 2009 California State University Distinguished Woman Award.
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