List of Code Challenges. About the Textbook Meet the Authors... xix Meet the Development Team... xx Acknowledgments... xxi

Size: px

Start display at page:

Download "List of Code Challenges. About the Textbook Meet the Authors... xix Meet the Development Team... xx Acknowledgments... xxi"

Beverly Bell
5 years ago
Views:

1 Contents List of Code Challenges xvii About the Textbook xix Meet the Authors xix Meet the Development Team xx Acknowledgments xxi 7 Which Animal Gave Us SARS? 2 The Fastest Outbreak... 3 Trouble at the Metropole Hotel... 3 The evolution of SARS... 3 Transforming Distance Matrices into Evolutionary Trees... 5 Constructing a distance matrix from coronavirus genomes... 5 Evolutionary trees as graphs... 7 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. 22 Ultrametric Evolutionary Trees The Neighbor-Joining Algorithm Transforming a distance matrix into a neighbor-joining matrix x

2 Analyzing coronaviruses with the neighbor-joining algorithm Limitations of distance-based approaches to evolutionary tree construction 33 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 How Did Yeast Become a Wine Maker? 68 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 xi

3 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 How Do We Locate Disease-Causing Mutations? 120 What Causes Ohdo Syndrome? Introduction to Multiple Pattern Matching Herding Patterns into a Trie Constructing a trie xii

4 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. 144 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 xiii

5 From suffix trees to suffix arrays From suffix arrays to suffix trees Binary search Bibliography Notes Why Have Biologists Still Not Developed an HIV Vaccine? 178 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 xiv

6 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 Was T. rex Just a Big Chicken? 234 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 xv

7 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 285 Image Courtesies 291 xvi

8 List of Code Challenges Chapter 7 2 (7A) Compute Distances Between Leaves (7B) Compute Limb Lengths in a Tree (7C) Implement ADDITIVEPHYLOGENY (7D) Implement UPGMA (7E) Implement NEIGHBORJOINING (7F) Implement SMALLPARSIMONY (7G) Adapt SMALLPARSIMONY to Unrooted Trees (7H) Find the Nearest Neighbors of a Tree (7I) Implement NEARESTNEIGHBORINTERCHANGE Chapter 8 68 (8A) Implement FARTHESTFIRSTTRAVERSAL (8B) Compute the Squared Error Distortion (8C) Implement the Lloyd Algorithm for k-means Clustering (8D) Implement the Soft k-means Clustering Algorithm (8E) Implement HIERARCHICALCLUSTERING Chapter (9A) Construct a Trie from a Collection of Patterns (9B) Implement TRIEMATCHING (9C) Construct the Suffix Tree of a String (9D) Find the Longest Repeat in a String (9E) Find the Longest Substring Shared by Two Strings (9F) Find the Shortest Non-Shared Substring of Two Strings (9G) Construct the Suffix Array of a String (9H) Implement PATTERNMATCHINGWITHSUFFIXARRAY xvii

9 (9I) Construct the Burrows-Wheeler Transform of a String (9J) Reconstruct a String from its Burrows-Wheeler Transform (9K) Generate the Last-to-First Mapping of a String (9L) Implement BWMATCHING (9M) Implement BETTERBWMATCHING (9N) Find All Occurrences of a Collection of Patterns in a String (9O) Find All Approximate Occurrences of a Collection of Patterns in a String 162 (9P) Implement TREECOLORING (9Q) Construct the Partial Suffix Array of a String (9R) Construct a Suffix Tree from a Suffix Array Chapter (10A) Compute the Probability of a Hidden Path (10B) Compute the Probability of an Outcome Given a Hidden Path (10C) Implement the Viterbi Algorithm (10D) Compute the Probability of a String Emitted by an HMM (10E) Construct a Profile HMM (10F) Construct a Profile HMM with Pseudocounts (10G) Perform a Multiple Sequence Alignment with a Profile HMM (10H) Estimate the Parameters of an HMM (10I) Implement Viterbi Learning (10J) Solve the Soft Decoding Problem (10K) Implement Baum-Welch Learning Chapter (11A) Construct the Graph of a Spectrum (11B) Implement DECODINGIDEALSPECTRUM (11C) Convert a Peptide into a Peptide Vector (11D) Convert a Peptide Vector into a Peptide (11E) Sequence a Peptide (11F) Find a Highest-Scoring Peptide in a Proteome against a Spectrum (11G) Implement PSMSEARCH (11H) Compute the Size of a Spectral Dictionary (11I) Compute the Probability of a Spectral Dictionary (11J) Find a Highest-Scoring Modified Peptide against a Spectrum xviii

List of Code Challenges. Meet the Authors Meet the Development Team... xxxii Meet our Adopting Institutions... xxxiv Acknowledgments...

List of Code Challenges. Meet the Authors Meet the Development Team... xxxii Meet our Adopting Institutions... xxxiv Acknowledgments... Contents List of Code Challenges xxv Meet the Authors xxxi Meet the Development Team............................ xxxii Meet our Adopting Institutions........................... xxxiv Acknowledgments..................................