{"product_id":"from-sequences-to-graphs-discrete-methods-and-structures-for-bioinformatics-9781789450668","title":"From Sequences to Graphs: Discrete Methods and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIn order to study living organisms, scientists not only study them at an overall macroscopic scale but also on a more detailed microscopic scale. This observation, pushed to its limits, consists of investigating the very center of each cell, where we find the molecules that determine the way it functions: DNA (deoxyribonucleic acid) and RNA (ribonucleic acid).\u003c\/p\u003e \u003cp\u003eIn an organism, DNA carries the genetic information, which is called the genome. It is represented as four-letter sequences using the letters A, C, G and T; based on these sequences, computer methods described in this book can answer fundamental questions in bioinformatics.\u003c\/p\u003e \u003cp\u003eThis book explores how to quickly find sequences of a few hundred nucleotides within a genome that may be made up of several billion, how to compare those sequences and how to reconstruct the complete sequence of a genome. It also discusses the problems of identifying bacteria in a given environment and predicting the structure of RNA based on its sequence.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAuthor Biographies xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Methodological Concepts: Algorithmic Solutions of Bioinformatics Problems 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnnie CHATEAU and Tom DAVOT-GRANGÉ\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Data, Models, Problem Formalism in Bioinformatics 1\u003c\/p\u003e \u003cp\u003e1.1.1 Data 1\u003c\/p\u003e \u003cp\u003e1.1.2 Genome Modeling 4\u003c\/p\u003e \u003cp\u003e1.1.3 Problems in Bioinformatics 5\u003c\/p\u003e \u003cp\u003e1.2 Mathematical Preliminaries 6\u003c\/p\u003e \u003cp\u003e1.2.1 Propositional Logic Preliminaries 6\u003c\/p\u003e \u003cp\u003e1.2.2 Preliminaries on Sets 7\u003c\/p\u003e \u003cp\u003e1.3 Vocabulary in Text Algorithmics 9\u003c\/p\u003e \u003cp\u003e1.4 Graph Theory 10\u003c\/p\u003e \u003cp\u003e1.4.1 Subgraphs 12\u003c\/p\u003e \u003cp\u003e1.4.2 Path in a Graph 13\u003c\/p\u003e \u003cp\u003e1.4.3 Matching 13\u003c\/p\u003e \u003cp\u003e1.4.4 Planarity 14\u003c\/p\u003e \u003cp\u003e1.4.5 Tree Decomposition 15\u003c\/p\u003e \u003cp\u003e1.5 Algorithmic Problems 16\u003c\/p\u003e \u003cp\u003e1.5.1 Definition 16\u003c\/p\u003e \u003cp\u003e1.5.2 Graph Problem 17\u003c\/p\u003e \u003cp\u003e1.5.3 Satisfiability Problems 19\u003c\/p\u003e \u003cp\u003e1.6 Problem Solutions 20\u003c\/p\u003e \u003cp\u003e1.6.1 Algorithm 20\u003c\/p\u003e \u003cp\u003e1.6.2 Complexity 21\u003c\/p\u003e \u003cp\u003e1.6.3 Runtime 24\u003c\/p\u003e \u003cp\u003e1.7 Complexity Classes 26\u003c\/p\u003e \u003cp\u003e1.7.1 Generality 26\u003c\/p\u003e \u003cp\u003e1.7.2 Exact Algorithms 28\u003c\/p\u003e \u003cp\u003e1.7.3 Approximation Algorithms 32\u003c\/p\u003e \u003cp\u003e1.7.4 Solvers 34\u003c\/p\u003e \u003cp\u003e1.8 Some Algorithmic Techniques 35\u003c\/p\u003e \u003cp\u003e1.8.1 Dynamic Programming 35\u003c\/p\u003e \u003cp\u003e1.8.2 Tree Traversal 38\u003c\/p\u003e \u003cp\u003e1.9 Validation 41\u003c\/p\u003e \u003cp\u003e1.9.1 The Different Types of Errors 42\u003c\/p\u003e \u003cp\u003e1.9.2 Quality Measures 44\u003c\/p\u003e \u003cp\u003e1.9.3 And in the Non-Binary Case? 46\u003c\/p\u003e \u003cp\u003e1.10 Conclusion 47\u003c\/p\u003e \u003cp\u003e1.11 References 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Sequence Indexing 49\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThierry LECROQ and Mikaël SALSON\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 49\u003c\/p\u003e \u003cp\u003e2.1.1 What is Indexing? 50\u003c\/p\u003e \u003cp\u003e2.1.2 When to Index? 51\u003c\/p\u003e \u003cp\u003e2.1.3 What to Index? 51\u003c\/p\u003e \u003cp\u003e2.1.4 Indexing Structures and Queries Considered 52\u003c\/p\u003e \u003cp\u003e2.1.5 Basic Notions and Vocabulary 53\u003c\/p\u003e \u003cp\u003e2.2 Word Indexing 54\u003c\/p\u003e \u003cp\u003e2.2.1 Bloom Filters 54\u003c\/p\u003e \u003cp\u003e2.2.2 Inverted List 56\u003c\/p\u003e \u003cp\u003e2.2.3 De Bruijn Graphs 60\u003c\/p\u003e \u003cp\u003e2.2.4 Efficient Structures for Targeted Queries 61\u003c\/p\u003e \u003cp\u003e2.3 Full-Text Indexing 62\u003c\/p\u003e \u003cp\u003e2.3.1 Suffix Tree 62\u003c\/p\u003e \u003cp\u003e2.3.2 (Extended) Suffix Array 64\u003c\/p\u003e \u003cp\u003e2.3.3 Burrows–Wheeler Transform 67\u003c\/p\u003e \u003cp\u003e2.4 Indexing Choice Criteria 76\u003c\/p\u003e \u003cp\u003e2.4.1 Based on the Type of the Necessary Query 77\u003c\/p\u003e \u003cp\u003e2.4.2 Based on the Space-Time and Data Quantity Trade-Off 77\u003c\/p\u003e \u003cp\u003e2.4.3 Based on the Need to Add or Modify Indexed Data 79\u003c\/p\u003e \u003cp\u003e2.4.4 Indexing Choices According to Applications 80\u003c\/p\u003e \u003cp\u003e2.5 Conclusion and Perspectives 81\u003c\/p\u003e \u003cp\u003e2.5.1 Efficient Methods for Indexing a Few Genomes or Sequencing Sets 81\u003c\/p\u003e \u003cp\u003e2.5.2 Methods that Struggle to Take Advantage of Data Redundancy 82\u003c\/p\u003e \u003cp\u003e2.6 References 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Sequence Alignment 87\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLaurent NOÉ\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 87\u003c\/p\u003e \u003cp\u003e3.1.1 What is Pairwise Alignment? 87\u003c\/p\u003e \u003cp\u003e3.1.2 How to Evaluate an Alignment? 88\u003c\/p\u003e \u003cp\u003e3.2 Exact Alignment 90\u003c\/p\u003e \u003cp\u003e3.2.1 Representation in Edit Graph Form 90\u003c\/p\u003e \u003cp\u003e3.2.2 Global Alignment and Needleman–Wunsch Algorithm 93\u003c\/p\u003e \u003cp\u003e3.2.3 Local Alignment and Smith–Waterman Algorithm 94\u003c\/p\u003e \u003cp\u003e3.2.4 Alignment with Affine Indel Function and the Gotoh Algorithm 96\u003c\/p\u003e \u003cp\u003e3.3 Heuristic Alignment 98\u003c\/p\u003e \u003cp\u003e3.3.1 Seeds 99\u003c\/p\u003e \u003cp\u003e3.3.2 \u003ci\u003eMin-Hash \u003c\/i\u003eand Global Sampling 105\u003c\/p\u003e \u003cp\u003e3.3.3 \u003ci\u003eMinimizing\u003c\/i\u003e and Local Sampling 106\u003c\/p\u003e \u003cp\u003e3.4 References 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Genome Assembly 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDominique LAVENIER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 113\u003c\/p\u003e \u003cp\u003e4.2 Sequencing Technologies 116\u003c\/p\u003e \u003cp\u003e4.2.1 Short Reads 117\u003c\/p\u003e \u003cp\u003e4.2.2 Long Reads 118\u003c\/p\u003e \u003cp\u003e4.2.3 Linked Reads 118\u003c\/p\u003e \u003cp\u003e4.2.4 Hi-C Reads 119\u003c\/p\u003e \u003cp\u003e4.2.5 Optical Mapping 119\u003c\/p\u003e \u003cp\u003e4.3 Assembly Strategies 120\u003c\/p\u003e \u003cp\u003e4.3.1 The Main Steps 120\u003c\/p\u003e \u003cp\u003e4.3.2 Cleaning and Correction of Reads 121\u003c\/p\u003e \u003cp\u003e4.3.3 Scaffold Construction 122\u003c\/p\u003e \u003cp\u003e4.3.4 Scaffold Ordering 123\u003c\/p\u003e \u003cp\u003e4.4 Scaffold Construction Methods 124\u003c\/p\u003e \u003cp\u003e4.4.1 Greedy Assembly 124\u003c\/p\u003e \u003cp\u003e4.4.2 OLC Assembly 126\u003c\/p\u003e \u003cp\u003e4.4.3 DBG Assembly 127\u003c\/p\u003e \u003cp\u003e4.4.4 Constrained Assembly 130\u003c\/p\u003e \u003cp\u003e4.5 Scaffold-Ordering Methods 132\u003c\/p\u003e \u003cp\u003e4.5.1 Hi-C Data-Based Methods 132\u003c\/p\u003e \u003cp\u003e4.5.2 Optical Mapping-Based Methods 137\u003c\/p\u003e \u003cp\u003e4.6 Assembly Validation 139\u003c\/p\u003e \u003cp\u003e4.6.1 Metrics 140\u003c\/p\u003e \u003cp\u003e4.6.2 Read Realignment 140\u003c\/p\u003e \u003cp\u003e4.6.3 Gene Prediction 141\u003c\/p\u003e \u003cp\u003e4.6.4 Competitions 141\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 142\u003c\/p\u003e \u003cp\u003e4.8 References 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Metagenomics and Metatranscriptomics 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCervin GUYOMAR and Claire LEMAITRE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 What is Metagenomics? 147\u003c\/p\u003e \u003cp\u003e5.1.1 Motivations and Historical Context 147\u003c\/p\u003e \u003cp\u003e5.1.2 The Metagenomics Data 148\u003c\/p\u003e \u003cp\u003e5.1.3 Bioinformatics Challenges for Metagenomics 151\u003c\/p\u003e \u003cp\u003e5.2 “Who Are They”: Taxonomic Characterization of Microbial Communities 153\u003c\/p\u003e \u003cp\u003e5.2.1 Methods for Targeted Metagenomics 154\u003c\/p\u003e \u003cp\u003e5.2.2 Whole-Genome Methods with Reference 155\u003c\/p\u003e \u003cp\u003e5.2.3 Reference-Free Methods 160\u003c\/p\u003e \u003cp\u003e5.3 “What Are They Able To Do?”: Functional Metagenomics 166\u003c\/p\u003e \u003cp\u003e5.3.1 Gene Prediction and Annotation 166\u003c\/p\u003e \u003cp\u003e5.3.2 Metatranscriptomics 167\u003c\/p\u003e \u003cp\u003e5.3.3 Reconstruction of Metabolic Networks 168\u003c\/p\u003e \u003cp\u003e5.4 Comparative Metagenomics 169\u003c\/p\u003e \u003cp\u003e5.4.1 Comparative Metagenomics with Diversity Estimation 170\u003c\/p\u003e \u003cp\u003e5.4.2 De Novo Comparative Metagenomics 170\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 175\u003c\/p\u003e \u003cp\u003e5.6 References 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 RNA Folding 185\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYann PONTY And Vladimir REINHARZ\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 185\u003c\/p\u003e \u003cp\u003e6.1.1 RNA Folding 186\u003c\/p\u003e \u003cp\u003e6.1.2 Secondary Structure 189\u003c\/p\u003e \u003cp\u003e6.2 Optimization for Structure Prediction 192\u003c\/p\u003e \u003cp\u003e6.2.1 Computing the Minimum Free-Energy (MFE) Structure 192\u003c\/p\u003e \u003cp\u003e6.2.2 Listing (Sub)optimal Structures 198\u003c\/p\u003e \u003cp\u003e6.2.3 Comparative Prediction: Simultaneous Alignment\/Folding of RNAs 203\u003c\/p\u003e \u003cp\u003e6.2.4 Joint Alignment\/Folding Model 204\u003c\/p\u003e \u003cp\u003e6.3 Analyzing the Boltzmann Ensemble 210\u003c\/p\u003e \u003cp\u003e6.3.1 Computing the Partition Function 210\u003c\/p\u003e \u003cp\u003e6.3.2 Statistical Sampling 215\u003c\/p\u003e \u003cp\u003e6.3.3 Boltzmann Probability of Structural Patterns 220\u003c\/p\u003e \u003cp\u003e6.4 Studying RNA Structure in Practice 225\u003c\/p\u003e \u003cp\u003e6.4.1 The Turner Model 225\u003c\/p\u003e \u003cp\u003e6.4.2 Tools 228\u003c\/p\u003e \u003cp\u003e6.5 References 228\u003c\/p\u003e \u003cp\u003eConclusion 233\u003c\/p\u003e \u003cp\u003eList of Authors 237\u003c\/p\u003e \u003cp\u003eIndex 239\u003c\/p\u003e","brand":"ISTE Ltd","offers":[{"title":"Default Title","offer_id":51042581774679,"sku":"9781789450668","price":112.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789450668.jpg?v=1750954733","url":"https:\/\/bookcurl.com\/products\/from-sequences-to-graphs-discrete-methods-and-structures-for-bioinformatics-9781789450668","provider":"Book Curl","version":"1.0","type":"link"}