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Bioinformatics Algorithms - vol2.pdf

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    List of Code Challenges
    About the Textbook
    Meet the Authors
    Meet the Development Team
    Acknowledgments
    Chapter 7: Which Animal Gave Us SARS?
    The Fastest Outbreak
    Trouble at the Metropole Hotel
    The evolution of SARS
    Transforming Distance Matrices into Evolutionary Trees
    Constructing a distance matrix from coronavirus genomes
    Evolutionary trees as graphs
    Distance-based phylogeny construction
    Toward An Algorithm for Distance-Based Phylogeny Construction
    A quest for neighboring leaves
    Computing limb lengths
    Additive Phylogeny
    Trimming the tree
    Attaching a limb
    An algorithm for distance-based phylogeny construction
    Constructing an evolutionary tree of coronaviruses
    Using Least Squares to Construct Approximate Distance-Based Phylogenies
    Ultrametric Evolutionary Trees
    The Neighbor-Joining Algorithm
    Transforming a distance matrix into a neighbor-joining matrix
    Analyzing coronaviruses with the neighbor-joining algorithm
    Limitations of distance-based approaches to evolutionary tree construction
    Character-Based Tree Reconstruction
    Character tables
    From anatomical to genetic characters
    How many times has evolution invented insect wings?
    The Small Parsimony Problem
    The Large Parsimony Problem
    Epilogue: Evolutionary Trees Fight Crime
    Detours
    When did HIV jump from primates to humans?
    Searching for a tree fitting a distance matrix
    The four point condition
    Did bats give us SARS?
    Why does the neighbor-joining algorithm find neighboring leaves?
    Computing limb lengths in the neighbor-joining algorithm
    Giant panda: bear or raccoon?
    Where did humans come from?
    Bibliography Notes
    Chapter 8: How Did Yeast Become a Wine Maker?
    An Evolutionary History of Wine Making
    How long have we been addicted to alcohol?
    The diauxic shift
    Identifying Genes Responsible for the Diauxic Shift
    Two evolutionary hypotheses with different fates
    Which yeast genes drive the diauxic shift?
    Introduction to Clustering
    Gene expression analysis
    Clustering yeast genes
    The Good Clustering Principle
    Clustering as an Optimization Problem
    Farthest First Traversal
    k-Means Clustering
    Squared error distortion
    k-means clustering and the center of gravity
    The Lloyd Algorithm
    From centers to clusters and back again
    Initializing the Lloyd algorithm
    k-means++ Initializer
    Clustering Genes Implicated in the Diauxic Shift
    Limitations of k-Means Clustering
    From Coin Flipping to k-Means Clustering
    Flipping coins with unknown biases
    Where is the computational problem?
    From coin flipping to the Lloyd algorithm
    Return to clustering
    Making Soft Decisions in Coin Flipping
    Expectation maximization: the E-step
    Expectation maximization: the M-step
    The expectation maximization algorithm
    Soft k-Means Clustering
    Applying expectation maximization to clustering
    Centers to soft clusters
    Soft clusters to centers
    Hierarchical Clustering
    Introduction to distance-based clustering
    Inferring clusters from a tree
    Analyzing the diauxic shift with hierarchical clustering
    Epilogue: Clustering Tumor Samples
    Detours
    Whole genome duplication or a series of duplications?
    Measuring gene expression
    Microarrays
    Proof of the Center of Gravity Theorem
    Transforming an expression matrix into a distance/similarity matrix
    Clustering and corrupted cliques
    Bibliography Notes
    Chapter 9: How Do We Locate Disease-Causing Mutations?
    What Causes Ohdo Syndrome?
    Introduction to Multiple Pattern Matching
    Herding Patterns into a Trie
    Constructing a trie
    Applying the trie to multiple pattern matching
    Preprocessing the Genome Instead
    Introduction to suffix tries
    Using suffix tries for pattern matching
    Suffix Trees
    Suffix Arrays
    Constructing a suffix array
    Pattern matching with the suffix array
    The Burrows-Wheeler Transform
    Genome compression
    Constructing the Burrows-Wheeler transform
    From repeats to runs
    Inverting the Burrows-Wheeler Transform
    A first attempt at inverting the Burrows-Wheeler transform
    The First-Last Property
    Using the First-Last property to invert the Burrows-Wheeler transform
    Pattern Matching with the Burrows-Wheeler Transform
    A first attempt at Burrows-Wheeler pattern matching
    Moving backward through a pattern
    The Last-to-First mapping
    Speeding Up Burrows-Wheeler Pattern Matching
    Substituting the Last-to-First mapping with count arrays
    Getting rid of the first column of the Burrows-Wheeler matrix
    Where are the Matched Patterns?
    Burrows and Wheeler Set Up Checkpoints
    Epilogue: Mismatch-Tolerant Read Mapping
    Reducing approximate pattern matching to exact pattern matching
    BLAST: Comparing a sequence against a database
    Approximate pattern matching with the Burrows-Wheeler transform
    Charging Stations
    Constructing a suffix tree
    Solving the Longest Shared Substring Problem
    Partial suffix array construction
    Detours
    The reference human genome
    Rearrangements, insertions, and deletions in human genomes
    The Aho-Corasick algorithm
    From suffix trees to suffix arrays
    From suffix arrays to suffix trees
    Binary search
    Bibliography Notes
    Chapter 10: Why Have Biologists Still Not Developed an HIV Vaccine?
    Classifying the HIV Phenotype
    How does HIV evade the human immune system?
    Limitations of sequence alignment
    Gambling with Yakuza
    Two Coins up the Dealer's Sleeve
    Finding CG-Islands
    Hidden Markov Models
    From coin flipping to a Hidden Markov Model
    The HMM diagram
    Reformulating the Casino Problem
    The Decoding Problem
    The Viterbi graph
    The Viterbi algorithm
    How fast is the Viterbi algorithm?
    Finding the Most Likely Outcome of an HMM
    Profile HMMs for Sequence Alignment
    How do HMMs relate to sequence alignment?
    Building a profile HMM
    Transition and emission probabilities of a profile HMM
    Classifying proteins with profile HMMs
    Aligning a protein against a profile HMM
    The return of pseudocounts
    The troublesome silent states
    Are profile HMMs really all that useful?
    Learning the Parameters of an HMM
    Estimating HMM parameters when the hidden path is known
    Viterbi learning
    Soft Decisions in Parameter Estimation
    The Soft Decoding Problem
    The forward-backward algorithm
    Baum-Welch Learning
    The Many Faces of HMMs
    Epilogue: Nature is a Tinkerer and not an Inventor
    Detours
    The Red Queen Effect
    Glycosylation
    DNA methylation
    Conditional probability
    Bibliography Notes
    Chapter 11: Was T. rex Just a Big Chicken?
    Paleontology Meets Computing
    Which Proteins Are Present in This Sample?
    Decoding an Ideal Spectrum
    From Ideal to Real Spectra
    Peptide Sequencing
    Scoring peptides against spectra
    Where are the suffix peptides?
    Peptide sequencing algorithm
    Peptide Identification
    The Peptide Identification Problem
    Identifying peptides in the unknown T. rex proteome
    Searching for peptide-spectrum matches
    Peptide Identification and the Infinite Monkey Theorem
    False discovery rate
    The monkey and the typewriter
    Statistical significance of a peptide-spectrum match
    Spectral Dictionaries
    T. rex Peptides: Contaminants or Treasure Trove of Ancient Proteins?
    The hemoglobin riddle
    The dinosaur DNA controversy
    Epilogue: From Unmodified to Modified Peptides
    Post-translational modifications
    Searching for modifications as an alignment problem
    Building a Manhattan grid for spectral alignment
    Spectral alignment algorithm
    Detours
    Gene prediction
    Finding all paths in a graph
    The Anti-Symmetric Path Problem
    Transforming spectra into spectral vectors
    The infinite monkey theorem
    The probabilistic space of peptides in a spectral dictionary
    Are terrestrial dinosaurs really the ancestors of birds?
    Solving the Most Likely Peptide Vector Problem
    Selecting parameters for transforming spectra into spectral vectors
    Bibliography Notes
    Bibliography
    Image Courtesies
    Welcome! Thank you for joining us! As you explore this book, you will find a number of active learning components that help you learn the material at your own pace. 1. CODE CHALLENGES ask you to implement the algorithms that you will en- counter (in any programming language you like). These code challenges are hosted in the “Bioinformatics Textbook Track” location on Rosalind (http:// rosalind.info), a website that will automatically test your implementations. 2. CHARGING STATIONS provide additional insights on implementing the algo- rithms you encounter. However, we suggest trying to solve a Code Challenge before you visit a Charging Station. 3. EXERCISE BREAKS offer “just in time” assessments testing your understanding of a topic before moving to the next one. 4. STOP and Think questions invite you to slow down and contemplate the current material before continuing to the next topic. 5. DETOURS provide extra content that didn’t quite fit in the main text. DETOUR 6. FINAL CHALLENGES ask you to apply what you have learned to real experi- mental datasets. This textbook powers our popular online courses on Coursera. We encourage you to sign up for a session and learn this material while interacting with thousands of other talented students from around the world. You can also find lecture videos and PowerPoint slides at the textbook website, http://bioinformaticsalgorithms.org. i
    Bioinformatics Algorithms:An Active Learning Approach2nd Edition, Vol. IIhttp://bioinformaticsalgorithms.org© 2015Phillip Compeau & Pavel Pevzner
    Copyright © 2015 by Phillip Compeau and Pavel Pevzner. All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatso- ever without the express written permission of the publisher except for the use of brief quotations in a book review. Printed in the United States of America First Printing, 2015 ISBN: 978-0-9903746-2-6 Library of Congress Control Number: 2015945208 Active Learning Publishers, LLC 9768 Claiborne Square La Jolla, CA 92037
    To my family. — P. C. To my parents. — P. P.
    In Case You Missed Volume I. . . CHAPTER 1 CHAPTER 2 vi
    CHAPTER 3 CHAPTER 4 CHAPTER 5 CHAPTER 6 vii
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