Computational Biology

This textbook is for anyone who needs to learn the basics of bioinformatics—the use of computational methods to better understand biological systems. Computational Biology covers the principles and applications of the computational methods used to study DNA, RNA, and proteins, including using biological databases such as NCBI and UniProt; performing BLAST, sequence alignments, and structural predictions; and creating phylogenetic trees. It includes a primer that can be used as a jumping off point for learning computer programming for bioinformatics.

This text can be used as a self-study guide, as a course focused on computational methods in biology/bioinformatics, or to supplement general courses that touch on topics included within the book. Computational Biology's robust interactive online components “gamify” the study of bioinformatics, allowing the reader to practice randomly generated problems on their own time to build confidence and skill and gain practical real-world experience. The online component also assures that the content being taught is up to date and accurately reflects the ever-changing landscape of bioinformatics web-based programs.

Preface ix

For the Instructor xi

For the Student xiii

Acknowledgments xiv

About the Authors xv

Chapter –1 Getting Started 1

Chapter 00 Introduction 5

Activity 0.1: Biological Databases and Data Storage 20

Chapter 01 BLAST 31

Activity 1.1: BLAST Algorithm 36

Chapter 02 Protein Analysis 47

Activity 2.1: Hydrophobicity Plotting 52

Activity 2.2: Protein Secondary Structure Prediction 58

Chapter 03 Sequence Alignment 67

Activity 3.1: Dynamic Programming 74

Chapter 04 Patterns in the Data 91

Activity 4.1: Protein Sequence Motifs 94

Activity 4.2: Position-Specific Weight Matrices 102

Chapter 05 RNA Structure Prediction 111

Activity 5.1: RNA Structure Prediction 118

Chapter 06 Phylogenetics 133

Activity 6.1: Phylogenetic Analysis 140

Chapter 07 Probability: All Mutations are not Equal (-ly Probable) 157

Activity 7.1: Generating PAM and BLOSUM Substitution Matrices 163

Chapter 08 Bioinformatics Programming: A Primer 179

Index 191

Scott T. Kelley is a professor of biology at San Diego State University. He received his doctoral degree from the University of Colorado and his bachelor's degree from Cornell University. His lab uses phylogenetic methods and culture-independent molecular tools to study environmental microbiology, and Kelley has published extensively on the human microbiome, built environment, and numerous natural environments.

Dennis Didulo is a data analytics and software engineer working at Becton, Dickinson and Company. He received his master's degree in information technology at De La Salle University and his second master's degree in bioinformatics at San Diego State University. He has development expertise in more than a dozen computer languages, as well as in database management, algorithm design, and systems engineering.

Computational Biology

First Edition

An introduction to the world of bioinformatics

Massive increases in computing power and the ability to routinely sequence whole genomes of living organisms have begun to fundamentally alter our understanding of biology, medicine, and agriculture. At the intersection of the growing information and genomics revolutions sits bioinformatics, which uses modern computational power to reveal patterns in biological data sets, especially DNA, RNA, and protein sequences.

Computational Biology: A Hypertextbook, by Scott Kelley and Dennis Didulo, provides a wonderful introduction for anyone who wants to learn the basics of bioinformatics. This book is more than a textbook because of the wealth of online ancillary materials and how the print and electronic components are integrated to form a complete educational resource. Aspects that make Computational Biology: A Hypertextbook a unique and valuable tool for teaching and learning bioinformatics include

Computational Biology: A Hypertextbook is written in an accessible voice, punctuated with humor, and designed to significantly increase computational competencies. Biology and computer science undergraduate and graduate students will thoroughly enjoy learning from this unique hypertextbook, as will anyone with an interest in exploring this burgeoning topic.