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Paperback 560 pp.
264 illustrations, 8.5" x 11"



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Concepts in Bioinformatics and Genomics

Jamil Momand and Alison McCurdy

Concepts in Bioinformatics and Genomics takes a conceptual approach, balancing biology, mathematics, and programming while highlighting relevant real-world applications and providing students with the tools to compute and analyze biological data. Through many thought-provoking exercises, students will develop a deeper understanding of the molecular biology, basic probability, software programs, and program-coding methodology underpinning this exciting field.

Readership : Momand's Concepts in Genomics and Bioinformatics is a comprehensive introduction to bioinformatics and genomics written for upper division science majors enrolled in their first bioinformatics and genomics courses.


  • "It is a real problem that bioinformatics requires aptitude in both biology and computing, yet the textbook market has not fully risen to meet this challenge for the undergraduates. I see this text as helping to bridge this gap. I would consider adopting this text because it is taking the biology-computation balance seriously and using what has become the dominant scripting language for biologists, Python."
    --Preston Aldrich, Bendectine University

  • "Concepts in Bioinformatics and Genomics is an excellent book covering bioinformatics concept essential for undergraduate students in bioinformatics majors, or biomedical informatics majors. What I like the most is the 'real life' sort of example and exercises provided throughout the chapters."
    --Mai Zahran, CUNY New York City College of Technology

  • "I am excited about this new bioinformatics book that is well-suited for either computer science students without much of a background in biology, or my more typical case of biology students without much of a background in computer science. I think the book will withstand the test of time in that it does not rely on ever-changing websites for practical exercises but on learning concepts and developing thought processes that will help students understand the field of bioinformatics. I think that this book will keep the students' interest as well as teach them all the principles of bioinformatics that they need to know."
    --James S. Godde, Monmouth College

  • "What makes this new textbook stand out are the following. First and foremost, it has a well-balanced coverage of the subject, both between breadth and depth, and between biology and computing as well. Its use of P53 as a case study throughout the text, threading together different aspects of bioinformatics, from alignment to phylogenetics, from structure prediction to gene expression analysis, is unique and remarkable, so is its integration of two chapters on programming basics and applications to bioinformatics, all based on Python, one of the most popular programming languages."
    --Li Liao, University of Delaware

  • "I like the approach of focusing on fundamentals, as well as flexibility of chapter ordering, which would work with my applications-focused approach where I can focus on specific modules depending on semester/class composition. Compared with my current text (Pevsner's 2nd ed), this is appealing."
    --Helen Piontkivska, Kent State University

  • "Prof. Jamil Momand book 'Concepts of Bioinformatics and Genomics' represents a solid addition to the list of existing bioinformatics textbooks. The approach taken by Prof. Jamil Momand, where he combines simply worded description of complex problems with exciting examples and historical facts makes this book an interesting read. In my view, this is exactly what we need."
    --Vladimir Uversky, University of South Florida

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
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
Answers to Thought Questions
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
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
Answers to Thought Questions
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
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
· Thought Question 6-2
6.3. Multiple Sequence Alignment (MSA)
Answers to Thought Questions
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
Answers to Thought Questions
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
Answers to Thought Questions
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?
Answers to Thought Questions
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
Answers to Thought Questions
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
Answers to Thought Questions
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
Answers to Thought Questions
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
Answers to Thought Questions
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
Answers to Thought Questions
I. Box 14-1. Scientist Spotlight: Richard Karp

There are no Instructor/Student Resources available at this time.

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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Special Features

  • Balanced presentation of biology, mathematics, and computer science highlights connections between the three areas and makes the material accessible to students from a variety of backgrounds.
  • Flexible organization through chapters designed as stand-alone units allows instructors to introduce biology, mathematics, and programming topics in order of preference.
  • Overview of molecular biology provides the essential biology concepts and vocabulary needed for understanding bioinformatics. (Ch. 1)
  • Mathematics chapters introduce basic probability as it leads up to the concept of Expect value (E-value) and its use in sequence alignment programs, including discussions of Hidden Markov chains. (Chs. 11, 12)
  • Two chapters devoted to basic programming introduce students to programming exercises directly related to bioinformatics problems, including hands-on work with Python--a popular and commonly used programming language in this field. (Chs. 13, 14)
  • Case studies discussing TP53, the p53 tumor suppressor gene, are woven throughout the text, providing students with insights to this clinically relevant gene.
  • Rich pedagogy--including in-text glossary terms, a comprehensive index, extensive footnotes, thought-provoking exercises, and more--supports students' learning.
  • Scientist Spotlight boxes feature biographies of pioneers in bioinformatics.