Concepts in Bioinformatics and Genomics

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

Table of Contents:
Preface About the Author 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 · 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 `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 · 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 Chapter 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 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 · 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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 Chapter 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) 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 · 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 Chapter 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 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 · 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 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 · 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 Chapter 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

About the Author :
Jamil Momand is Professor of Biochemistry at California State University, Los Angeles. He received the Cal State LA Outstanding Professor Award for the 2014-2015 academic year. Alison McCurdy is Professor of Chemistry at California State University, Los Angeles. She was the recipient of the 2009 California State University Distinguished Woman Award.

Review :
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 "First and foremost, this text has well-balanced coverage of the subject, in both breadth and depth, between biology and computing. The overall presentation is pedagogically well crafted with a lot of concrete examples and graphics. I am sure that this book will positively impact the teaching of bioinformatics."--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 "After using this text students will be able to grasp difficult areas of computational biology and gain an appreciation for and excitement about the potential that bioinformatics offers."--Erich Baker, Baylor University