Understanding Bioinformatics

Citation

Zvelebil, M., & Baum, J. O. (2007). Understanding Bioinformatics. Garland Science.

Chapter Summary

The book has seventeen chapters in seven parts and about 800 pages. After the opening part on background, each part pairs an "Applications Chapter" with one or more "Theory Chapters". The publisher describes the Applications Chapter as a fast route to the main concepts and to getting started with the analysis, and each Theory Chapter as giving greater detail and the underlying mathematics. The chapter titles below are those recorded in the publisher's chapter list.

Part 1, Background Basics: Chapters 1–3

"The Nucleic Acid World", "Protein Structure" and "Dealing With Databases" introduce the raw material of bioinformatics: nucleic acid sequences, proteins and databases.

Part 2, Sequence Alignments: Chapters 4–6

Chapter 4, "Producing and Analyzing Sequence Alignments" (Applications), shows how to produce and analyse alignments and use sequences for database searching. Chapter 5, "Pairwise Sequence Alignment and Database Searching" (Theory), and Chapter 6, "Patterns, Profiles, and Multiple Alignments" (Theory), then examine the more advanced techniques and the mathematical algorithms behind them.

Part 3, Evolutionary Processes: Chapters 7–8

Chapter 7, "Recovering Evolutionary History" (Applications), guides the reader through recovering evolutionary history from DNA data. Chapter 8, "Building Phylogenetic Trees" (Theory), gives the detail of constructing a phylogenetic tree.

Part 4, Genome Characteristics: Chapters 9–10

Chapter 9, "Revealing Genome Features" (Applications), covers the basics of gene prediction. Chapter 10, "Gene Detection and Genome Annotation" (Theory), elaborates on the basic and more advanced techniques.

Part 5, Secondary Structures: Chapters 11–12

Chapter 11, "Obtaining Secondary Structure from Sequence" (Applications), gives an overview of web-based techniques for predicting secondary structure and applying them. Chapter 12, "Predicting Secondary Structures" (Theory), covers the algorithms behind those techniques.

Part 6, Tertiary Structures: Chapters 13–14

Chapter 13, "Modeling Protein Structure" (Applications), looks at homology modelling, threading and ab initio modelling of tertiary structure. Chapter 14, "Analyzing Structure–Function Relationships" (Applications), analyses how structure relates to function.

Part 7, Cells and Organisms: Chapters 15–17

"Proteome and Gene Expression Analysis", "Clustering Methods and Statistics" and "Systems Biology" cover the analysis of expression data from the genes or proteins of an organism, statistical analysis and classification, and the emerging field of systems biology. The book also includes appendices on background theory.

Key Concepts

Part 1: background

• Sequences and databases: nucleic acids and proteins as the data, and public databases as the place they are kept and searched.

Parts 2–3: alignment and evolution

• Pairwise and multiple sequence alignment: aligning sequences to find similarity and, through patterns and profiles, conserved features.
• Database searching: using alignment to find related sequences.
• Phylogenetic trees: reconstructing evolutionary history from sequence data.

Parts 4–6: genomes and structure

• Gene prediction and genome annotation: locating genes in genomic sequence and describing their features.
• Secondary structure prediction: predicting local structure from sequence.
• Tertiary structure modelling: homology modelling, threading and ab initio modelling.
• Structure–function analysis: relating a protein's structure to what it does.

Part 7: expression and systems

• Expression analysis and clustering: grouping genes or proteins by their patterns of expression.
• Systems biology: an overview of the field, presented at the end of the book.

Critical Analysis

Strengths

• Applications and theory are separated. The paired chapters let readers who want to use the tools stop at the Applications Chapter, while those who want the mathematics can continue. The publisher presents this as the book's distinctive feature.
• Conceptual approach and visual design. The publisher says each chapter uses mind-maps and flow diagrams to give an overview of the conceptual links in its topic, and that the full-colour artwork is designed to present concepts simply and consistently.
• Reviewers' comments. Quotations on the publisher's page describe the book as comprehensive on bioinformatics algorithms and techniques (Jaap Heringa, Free University Amsterdam), as explaining gene finding in detail compared with other textbooks (Sun Kim, Indiana University), and as well balanced between applications and theory (Computer Methods and Programs in Biomedicine).
• Whole workflow. The parts follow the usual route from sequences to alignments, evolution, genomes, structures and expression, ending in systems biology.

Limitations

• Size and mathematics. At about 800 pages with Theory Chapters that "present the underlying mathematics", the book is a substantial commitment, and the theory half is for readers comfortable with algorithms and probability.
• Web tools age. The Applications Chapters rely on web-based tools and databases (for example, for secondary structure prediction), and a book published in 2007 cannot show the current form of those services or cover later methods.
• Methods rather than programming. The publisher's description concerns concepts, algorithms and mathematics, and mentions no programming exercises. Readers who want to implement the algorithms themselves will need a problem-driven text.

Where It Fits in the Course

Compared with the other bioinformatics books on the list, this is the long, balanced reference. Lesk's Introduction to Bioinformatics presents the field in plain language and emphasises how results are interpreted, and is the gentler starting point. Compeau and Pevzner's Bioinformatics Algorithms is a problem-driven introduction in which every chapter starts from a biological question and develops the algorithm needed. Baxevanis, Bader and Wishart's Bioinformatics is an edited textbook on databases, algorithms and analysis methods.

Zvelebil and Baum sit between a first course and the research literature. They suit readers who have met the tools and now want to understand how alignment, tree-building, gene finding and structure prediction work. Alon's An Introduction to Systems Biology goes much further than the closing systems-biology chapter.

How to Use This Book

The book is for advanced undergraduates and postgraduates (the publisher's audience) in biology, computer science and related fields. Readers should have introductory molecular biology and some comfort with algorithms and basic probability for the Theory Chapters.

A practical plan:

• Fast route: Chapters 1–3, then only the Applications Chapters (4, 7, 9, 11, 13 and 14), working with real sequences on the web servers described.
• Full route: read each Applications Chapter and then its Theory Chapters in order, especially Chapters 5–6 (alignment algorithms) and Chapter 8 (tree building).
• Optional: Chapter 10 for gene finding in depth, and Chapters 15–17 for expression data and systems biology.
• Combine the book with hands-on analysis of sequence data of your own, using the tools the Applications Chapters introduce.