Concepts in Bioinformatics and Genomics

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.

• Concepts in Bioinformatics and Genomics (Detailed Table of Contents)PrefaceAbout the AuthorChapter I: Review of Molecular Biology1.1 Genes and DNA1.2 RNA-the intermediary1.3 Amino acids-the building blocks of proteins1.4 Levels of protein structure1.5 The genetic code1.6 Relative sizes of matter1.7 DNA alterations1.8 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 mutation1.9 Introduction to p53ExercisesReferencesBox 1-1. A Closer Look: A rare inherited cancer is caused by mutated Tp53Chapter 2: Information organization and sequence databases2.1 Introduction2.2 Public databases2.3 The header2.4 The feature keys · The CDS feature key and gene structure· The gene feature key and FASTA format· Thought Question 2.12.5 Limitations of GenBank2.6 Reference Sequence (RefSeq)· Alternative splicing 2.7 Primary and secondary databases· The UniProt Knowledge Base (UniProtKB) databaseExercisesAnswers to Thought QuestionsReferencesBox 2-1. A Closer Look: GenBank is Critical to the Discovery of the MDM2 Oncoprotein-an Inhibitor of p53Chapter 3: Molecular Evolution3.1 Introduction3.2 Conserved regions in proteins3.3 Molecular Evolution· Transformation of normal cells to cancer cells· Are mutations inherited?· Natural selection· Mechanisms of mutation3.4 Ancestral genes and protein evolution3.5 Modular proteins and protein evolutionExercisesReferencesChapter 4: Substitution matrices4.1 Introduction4.2 The identity substitution matrix4.3 An amino acid substitution system based on natural selection4.4 Development of the matrix of "accepted" amino acid substitutions· Thought Question 4-14.5 Relative mutability calculations4.6 Development of the PAM1 mutation probability matrix4.7 Determination of the relative frequencies of amino acids4.8 Conversion of the PAM1 mutation probability matrix to the PAM1 log-odds substitution matrix4.9 Conversion of the PAM1 mutational probability matrix to other PAM 4.10 Practical uses for PAM substitution matrices4.11 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 usageExercisesAnswers to Thought QuestionsReferences Chapter 5: Pairwise sequence alignment5.1 Introduction5.2 Sliding window· Dot plots· The Dotter program5.3 The Needleman-Wunsch global alignment program· Initialization and matrix fill· Traceback· Gap penalties5.4 Modified Needleman-Wunsch global alignment (N-Wmod) program with linear gap penalty· N-Wmod initialization· N-Wmod matrix fill· N-Wmod traceback5.5 Ends-free global alignment5.6 Local alignment algorithm with linear gap penaltyExercisesReferencesChapter 6: Basic Local Alignment Sequence Tool and Multiple Sequence Alignment6.1 Introduction6.2 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 6-26.3. Multiple Sequence Alignment (MSA)· CLUSTALWExercisesAnswers to Thought QuestionsReferencesChapter 7: Protein structure prediction7.1 Introduction7.2 Experimental methods of structure determination· X-ray crystallography· NMR spectroscopy7.3 Information deposited into the Protein Data Bank7.4 Molecular viewers· Thought question 7-17.5 Protein folding· Christian Anfisen's protein unfolding and refolding experiment· Local minimum energy states· Energy Landscape theory7.6 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: ThreadingExercisesAnswers to Thought QuestionsReferencesBox 7-1. A Closer Look: p53 co-crystallized with DNA reveals insights into cancerChapter 8: Phylogenetics8.1 Introduction8.2 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 trees8.3 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 humans8.5 Phylogenetic Tree of Life8.6 The Tp53 gene family members in different speciesExercisesAnswers to Thought QuestionsReferences Box 8-1. A Closer Look: What do we know about Neanderthal and Denisovan?Chapter 9. Genomics9.1 Introduction9.2 DNA sequencing-dideoxy method · Dideoxy nucleotides· The step-by-step procedure of DNA sequencing· Electrophoresis· Thought question 9-19.3 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 sequencing9.5 The PhiX174 bacteriophage genome9.6 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. genome9.7 Genome assembly and annotation· Contig N50 and scaffold N50· Bacterial genome annotation systems9.8 Genome comparisons· Synteny Dotplot· Comparison of E. coli Substrain DH10B to E. coli Substrain MG16559.10 The human genome· General characteristics of the human genome· Thought question 3· Detailed analysis of the human genome landscape9.11 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 genome9.12 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 database9.13 Practical application of Tag SNP, SNP and mutation analyses9.14 What is the smallest genome?ExercisesAnswers to Thought QuestionsReferencesBox 9-1. A Closer Look: DNA Fingerprinting (DNA Profiling)Chapter 10. Transcript and protein expression analysis10.1 Introduction10.2 Basic principles of gene expression10.3 Measurement of transcript levels· Thought question 10-110.4 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 rate10.5 RNA-seq (RNA sequencing)· Advantages of RNA-seq· Overview of RNA-seq steps· Bridge amplification· Analysis of an experiment using RNA-seq10.6 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 techniques10.7 Regulation of p53-controlled genesExercisesAnswers to Thought QuestionsReferencesChapter 11. Basic probability11.1 Introduction11. 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 11-211.3 Random variables· Discrete random variables· Thought Question 11-3· Thought Question 11-4· Continuous random variablesExercisesAnswers to Thought QuestionsReferencesChapter 12. Advanced probability for bioinformatics applications12.1 Introduction12.2 Extreme value distribution12.3 Significance of alignments12.4 Stochastic processes· Markov chains· Thought Question 12-1· Hidden Markov models· Poisson process and Jukes-Cantor ModelExercisesAnswers to Thought QuestionsReferencesChapter 13. Programming basics and applications to bioinformatics13.1 Introduction13.2 Developers and users work together to make new discoveries. 13.3 Why Python? 13.4 Getting started with Python13.5 Data flow: representing and manipulating data· Variable names· Data types and operators13.6 Putting it together-a simple program to lookup the hydrophobicity of an amino acid13.7 Decision making· Operations for decision making· If-tests· Conditional expressions· Loops· Thought Question 13-1· Thought Question 13-2· Thought Question 13-313.8 Input and output13.9 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 13-413.10 Hierarchical design: functions and modules· Python functions· Thought Question 13.5· Python modules and packagesExercisesAnswers to Thought QuestionsReferencesChapter 14. Developing a bioinformatics tool14.1 Introduction14.2 Analysis of an existing tool: EMBOSS water local alignment tool· Thought question 14.3 Overview of SPA: A simple pairwise alignment tool14.4 Algorithms14.5 Algorithms for SPA· Input sequences· Create substitution matrix· Input gap penalties· Suite of pairwise sequence alignment algorithms· Output alignment14.6 Algorithm complexity14.7 Extensions to simple pairwise alignment toolExercisesProjectAnswers to Thought QuestionsReferencesGlossaryIndex

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.