زیست‌شناسی محاسباتی: یک هایپرکتاب

He has a Ph.D. from the University of Colorado and a B.A. from Cornell University. His lab at San Diego State University combines phyloge ne tic methods and culture-independent molecular tools to study environmental microbiology. Dr. Kelley has published extensively on the human microbiome, the built environment, and many natu ral environments. He has published many papers on bioinformatics, and has helped develop some widely-used tools for analyzing next-generation sequence data sets for microbial communities. CONTENTS PREFACE FOR THE INSTRUCTOR FOR THE STUDENT ACKNOWLEDGMENTS ABOUT THE AUTHORS CHAPTER -1: GETTING STARTED Using the Website Example: The Alignment Page How To Use This Book CHAPTER 00: INTRODUCTION Why Bioinformatics? DNA in the Computer RNA in the Computer Protein Translation Protein Sequences in the Computer The Molecular Structure of a Gene Notes ACTIVITY 0.1: BIOLOGICAL DATABASES AND DATA STORAGE Motivation Learning Objectives Concepts Exercises Lab exercises (practice) Lab Exercise Part 1: using NCBI PubMed Part 2: data formats Part 3: microbial genomes and Uniprot databases CHAPTER 01: BLAST BLAST It Scaling Up: Massive Parallelization of BLAST Why a Nobel Prize? Notes ACTIVITY 1.1: BLAST ALGORITHM Motivation Learning Objectives Concepts Reflection Exercises Interactive exercise (theory) Problem Lab Exercises (Practice) Lab Exercise Part 1 Part 2 CHAPTER 02: PROTEIN ANALYSIS Protein Bioinformatics Bioinformatics Methods Notes ACTIVITY 2.1: HYDROPHOBICITY PLOTTING Motivation Learning Objectives Concepts Reflection Exercises Interactive exercise (theory) Problem Lab Exercises (Practice) Lab Exercise ACTIVITY 2.2: PROTEIN SECONDARY STRUCTURE PREDICTION Motivation Learning Objectives Concepts Calculating Propensities Reflection 1 Where's Alphie? Like "Where's Waldo" but not as frustrating! Reflection 2 Exercises Interactive exercise (theory) Problem Lab Exercises (Practice) Lab Exercise CHAPTER 03: SEQUENCE ALIGNMENT What Is a Sequence Alignment? Sequence Alignments: Nature’s Experimental Results What Are the Challenges in Aligning Sequences? Issues in Sequence Alignment Multiple-Sequence Alignment Notes ACTIVITY 3.1: DYNAMIC PROGRAMMING Motivation Learning Objectives Concepts Reflection Finding the path through the graph Exercises Interactive exercise (theory) Problems Lab Exercises (Practice) Lab Exercise DNA multiple-sequence alignment Protein multiple-sequence alignment Notes CHAPTER 04: PATTERNS IN THE DATA Sequence Motifs Notes ACTIVITY 4.1: PROTEIN SEQUENCE MOTIFS Motivation Learning Objectives Concepts Reflection Exercises Interactive exercises (theory) Problem Lab Exercises (Practice) Lab Exercise Notes ACTIVITY 4.2: POSITION-SPECIFIC WEIGHT MATRICES Motivation Learning Objectives Concepts Reflection Exercises Interactive exercises (theory) Problem Lab Exercises (Practice) Lab Exercise Notes CHAPTER 05: RNA STRUCTURE PREDICTION Roles of RNA in Cells Predicting RNA Structure Notes ACTIVITY 5.1: RNA STRUCTURE PREDICTION Motivation Learning Objectives Concepts Algorithm 1: thermodynamic secondary-structure prediction Reflection Calculating RNA free energy Comparing possible structures Algorithm 2: Mutual Information (MI) Reflection Exercises Interactive exercises (theory) Problems Lab Exercises (Practice) Lab Exercise Part 1. RNA folding Part 2 Notes CHAPTER 06: PHYLOGENETICS Ramifications of the “Big Tree” Uses of Phylogenetics How To Interpret Phylogenetic Trees The Bootstrap Notes ACTIVITY 6.1: PHYLOGENETIC ANALYSIS Motivation Learning Objectives Concepts Algorithm 1: distance method Reflection Using distances to build a phylogenetic tree Algorithm 2: maximum parsimony (MP) method Reflection Which phylogenetic tree is the shortest (most parsimonious)? Exercises Interactive exercises (theory) Problems Lab Exercises (Practice) Lab Exercise Notes CHAPTER 07: PROBABILITY: ALL MUTATIONS ARE NOT EQUAL (-LY PROBABLE) Protein (Amino Acid) Substitution Matrices What Determines Substitution Bias? PAM and BLOSUM Hidden Markov Models Notes ACTIVITY 7.1: GENERATING PAM AND BLOSUM SUBSTITUTION MATRICES Motivation Learning Objectives Concepts Reflection Sweet Lou Calculating a PAM Matrix Calculating a BLOSUM Matrix Exercises Interactive exercise (theory) Problems Solving for a BLOSUM matrix Lab Exercises (Practice) Lab Exercise Part 1. BLOSUM and PAM: using blastp advanced parameters Part 2. Pfam database Notes CHAPTER 08: BIOINFORMATICS PROGRAMMING: A PRIMER The Unix Operating System A short Unix tutorial Introduction to R Introduction to Python Notes INDEX 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: Clear explanations of the basic biology of DNA, RNA, and proteins and how the related bioinformatics algorithms work; Extensive exercises that enable students to practice with the same bioinformatics applications that are used by scientists worldwide; Tutorials, sample data sets, and interactive learning tools developed with teachers in mind and field-tested by hundreds of students; Online tutorials and curated web links that are accurate (instead of frustrating!) and wont lead to dead ends; Online resources that work on multiple platforms and electronic devices. 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. 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 bachelors 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 masters degree in information technology at De La Salle University and his second masters 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. Massive increases in computing power and the ability to routinely sequence whole genomes of living organisms have begun to fundamentally alter our understanding 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 to 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 for form a complete educational resource. Aspects that make Computational Biology: A Hypertextbook a unique and valuable tool for teaching and learning bioinformatics include: Clear explanations of the basic biology of DNA, RNA, and proteins and how the related bioinformatics algorithms work; Extensive exercises that enable students to practice with the same bioinformatics applications that are used by scientists worldwide; Tutorials, sample data sets, and interactive learning tools developing with teachers in mind and field-tested by hundreds of students; Online tutorials and curated web links that are accurate (instead of frustrating!) and won't lead to dead ends; Online resources that work on multiple platforms and electronic devices. 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. -- From back cover