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Preface

In 1982, the first release of the GenBank sequence database contained 601,438 residues. By 2005, this number had grown beyond and continues to increase exponentially. Far from regarding this as “information overload”, we believe the free availability of so much precise and fundamental data on the ultimate constituents of life to be the hallmark of a golden age in biomedical research. Computational biology is concerned with helping to understand these data. The aim of this book is to give a first introduction to the computational aspects of genome-scale molecular biology, also known as genomics. The interpretation of biological data is often contingent on an understanding of the evolutionary history that has generated it. Hence, we explain evolutionary models as well as classical sequence analysis. Our intended audience is primarily students of bioinformatics, as well as researchers and students in neighboring disciplines including molecular biology, genetics, medicine, physics, mathematics, and computer science. As background, we assume familiarity with basic general and molecular biology as well as elementary probability theory. We also expect an interest in computers and their programming. In writing this book we have benefited from the expertise and support of a number of colleagues. Clemens Beckstein invited us in 1999 to give our first lecture series on computational biology at Jena University. Wolfgang Stephan and Monty Slatkin encouraged us to turn the lecture notes accumulated in Jena into a textbook. Steffi Gebauer-Jung helped with some of the algorithms we present. Claudia Acquisti, Frank Leßke, Peter Pfaffelhuber, Karl Schmid, and Daniel Zivkovic commented on earlier versions of the manuscript. Our students improved our teaching of computational biology over the years. Finally, we owe a huge debt of gratitude to Angelika B¨orsch-Haubold, who edited the entire book, compiled the index and guided this project through the production stage. Without her contribution there would be no book.

Freising and K¨oln, Bernhard Haubold March 2006 Thomas Wiehe

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. 1 Reading and Writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. 2 Design and Scope of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. 2.1 Sequences in Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. 2.2 Sequences in Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I Sequences in Space

Optimal Pairwise Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2. 1 What Is an Alignment? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. 2 Biological Interpretation of the Alignment Problem . . . . . . . . . . . . .
4. 3 Scoring Alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. 4 Amino Acid Substitution Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . .
6. 4.1 PAM Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7. 4.2 BLOSUM Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. 4.3 Comparison between PAM and BLOSUM . . . . . . . . . . . . . .
9. 4.4 Application of Substitution Matrices . . . . . . . . . . . . . . . . . . .
10. 5 The Number of Possible Alignments . . . . . . . . . . . . . . . . . . . . . . . . .
11. 6
12. 7 Shotgun Sequencing and Overlap Alignment . . . . . . . . . . . . . . . . . .
13. 8
14. 9 Accommodating Affine Gap Costs . . . . . . . . . . . . . . . . . . . . . . . . . . .
15. 10 Maximizing vs. Minimizing Scores. . . . . . . . . . . . . . . . . . . . . . . . . . .
16. 11 Example Application of Global, Local, and Overlap Alignment . . .
17. 12 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18. 13
19. 14