Bioinformatics algorithms : design and implementation in Python

'Bioinformatics Algorithms' provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becomi...

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Bibliographic Details
Main Authors Rocha, Miguel P., Ferreira, Pedro G.
Format eBook Book
LanguageEnglish
Published London Academic Press, an imprint of Elsevier 2018
Elsevier Science & Technology
Academic Press
Edition1
Subjects
Algorithms
Bioinformatics
Bioinformatics-Data processing
Online AccessGet full text
ISBN0128125209
9780128125205
DOI10.1016/C2016-0-02165-4

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Abstract 'Bioinformatics Algorithms' provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications.
AbstractList 'Bioinformatics Algorithms' provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications.
Author Rocha, Miguel P.
Ferreira, Pedro G.
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Notes Includes bibliographical references (p. 375-381) and index
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Snippet 'Bioinformatics Algorithms' provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and...
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SubjectTerms Algorithms
Bioinformatics
Bioinformatics-Data processing
TableOfContents Bibliographical Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 7 Searching Similar Sequences in Databases -- 7.1 Introduction -- 7.2 BLAST Algorithm and Programs -- 7.2.1 Overview of the BLAST Algorithm -- 7.2.2 BLAST Programs -- 7.2.3 Signi cance of the Alignments -- 7.3 Implementing Our Own BLAST -- 7.4 Using BLAST Through BioPython -- Bibliographical Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 8 Multiple Sequence Alignment -- 8.1 Introduction: Problem De nition and Complexity -- 8.2 Classes of Optimization Algorithms for Multiple Sequence Alignment -- 8.2.1 Dynamic Programming -- 8.2.2 Heuristic Algorithms -- 8.3 Implementing Progressive Alignments in Python -- 8.3.1 Representing Alignments: Class MyAlign -- 8.3.2 Pairwise Alignment: Class AlignSeq -- 8.3.3 Implementing Multiple Sequence Alignment: Class MultipleAlign -- 8.4 Handling Alignments in BioPython -- Bibliographical Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 9 Phylogenetic Analysis -- 9.1 Introduction: Problem De nition and Relevance -- 9.2 Classes of Algorithms for Phylogenetic Analysis -- 9.2.1 Distance-Based Methods -- 9.2.2 Maximum Parsimony -- 9.2.3 Statistical Methods -- 9.3 Implementing Distance-Based Algorithms in Python -- 9.3.1 Implementing Binary Trees -- 9.3.2 Implementing the UPGMA Algorithm -- 9.4 BioPython Functions for Phylogenetic Analysis -- Bibliographical Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 10 Motif Discovery Algorithms -- 10.1 Introduction: Problem De nition and Relevance -- 10.2 Brute-Force Algorithms: Exhaustive Search -- 10.3 Branch-and-Bound Algorithms -- 10.4 Heuristic Algorithms
Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 11 Probabilistic Motifs and Stochastic Algorithms -- 11.1 Representing and Searching Probabilistic Motifs -- 11.2 Stochastic Algorithms: Expectation-Maximization -- 11.3 Gibbs Sampling for Motif Discovery -- 11.4 Probabilistic Motifs in BioPython -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 12 Hidden Markov Models -- 12.1 Introduction: What Are Hidden Markov Models? -- 12.2 Algorithms and Python Implementation -- 12.2.1 Joint Probability of an Observed Sequence and State Path -- 12.2.2 Probability of an Observed Sequence Over All State Paths -- 12.2.3 Probability of the Remainder of an Observed Sequence -- 12.2.4 Finding the Optimal State Path -- 12.2.5 Learning the Parameters of an HMM Model -- 12.3 HMMs for Database Search -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- 13 Graphs: Concepts and Algorithms -- 13.1 Graphs: De nitions and Representations -- 13.2 A Python Class for Graphs -- 13.3 Adjacent Nodes and Degrees -- 13.4 Paths, Searches, and Distances -- 13.5 Cycles -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 14 Graphs and Biological Networks -- 14.1 Introduction -- 14.2 Representing Networks With Graphs -- 14.2.1 A Python Class for Metabolic Networks -- 14.2.2 An Example Metabolic Network for a Real Organism -- 14.3 Network Topological Analysis -- 14.3.1 Degree Distribution -- 14.3.2 Shortest Path Analysis -- 14.3.3 Clustering Coef cients -- 14.3.4 Hubs and Centrality Measures -- 14.4 Assessing the Metabolic Potential -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects
15 Assembling Reads Into Genomes: Graph-Based Algorithms -- 15.1 Introduction to Genome Assembly and Related Challenges -- 15.2 Overlap Graphs and Hamiltonian Cycles -- 15.2.1 Problem De nition and Exhaustive Search -- 15.2.2 Overlap Graphs -- 15.2.3 Hamiltonian Circuits -- 15.3 DeBruijn Graphs and Eulerian Paths -- 15.3.1 DeBruijn Graphs for Genome Assembly -- 15.3.2 Eulerian Paths -- 15.4 Genome Assembly in Practice -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 16 Matching Reads to Reference Sequences -- 16.1 Introduction: Problem De nition and Applications -- 16.2 Pre-Processing the Patterns: Tries -- 16.2.1 De nitions and Algorithms -- 16.2.2 Implementing Tries in Python -- 16.3 Pre-Processing the Sequence: Suf x Trees -- 16.3.1 De nitions and Algorithms -- 16.3.2 Implementing Suf x Trees in Python -- 16.4 Burrows-Wheeler Transforms -- 16.4.1 De nitions and Algorithms -- 16.4.2 Implementation in Python -- 16.4.3 Aligning References to Genomes in Practice -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 17 Further Reading and Resources -- 17.1 Complementary Books -- 17.2 Journals and Conferences -- 17.3 Formal Education -- 17.4 Online Resources -- Final Words -- Bibliography -- Index -- Back Cover
Bibliographic References and Further Reading -- Exercises -- 4 Basic Processing of Biological Sequences -- 4.1 Biological Sequences: Representations and Basic Algorithms -- 4.2 Transcription and Reverse Complement -- 4.3 Translation -- 4.4 Seeking Putative Genes: Open Reading Frames -- 4.5 Putting It All Together -- 4.6 A Class for Biological Sequences -- 4.7 Processing Sequences With BioPython -- 4.8 Sequence Annotation Objects in BioPython -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 5 Finding Patterns in Sequences -- 5.1 Introduction: Importance of Pattern Finding in Bioinformatics -- 5.2 Naive Algorithm for Fixed Pattern Finding -- 5.3 Heuristic Algorithm: Boyer-Moore -- 5.4 Deterministic Finite Automata -- 5.5 Finding Flexible Patterns: Regular Expressions -- 5.5.1 De nitions and Regular Expressions in Python -- 5.5.2 Examples in Biological Sequence Analysis -- 5.5.3 Finding Protein Motifs -- 5.5.4 An Application to Restriction Enzymes -- Bibliographic Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 6 Pairwise Sequence Alignment -- 6.1 Introduction: Comparing Sequences and Sequence Alignment -- 6.2 Visual Alignments: Dot Plots -- 6.3 Sequence Alignment as an Optimization Problem -- 6.3.1 Problem De nition and Complexity -- 6.3.2 Objective Function: Substitution Matrices and Gap Penalties -- 6.3.3 Implementing the Calculation of the Objective Function -- 6.4 Dynamic Programming Algorithms for Global Alignment -- 6.4.1 The Needleman-Wunsch Algorithm -- 6.4.2 Implementing the Needleman-Wunsch Algorithm -- 6.5 Dynamic Programming Algorithms for Local Alignment -- 6.5.1 The Smith-Waterman Algorithm -- 6.5.2 Implementing the Smith-Waterman Algorithm -- 6.6 Special Cases of Sequence Alignment -- 6.7 Pairwise Sequence Alignment in BioPython
Front Cover -- Bioinformatics Algorithms -- Copyright -- Contents -- 1 Introduction -- 1.1 Prelude -- 1.2 What is Bioinformatics -- 1.3 Book's Organization -- 2 An Introduction to the Python Language -- 2.1 Features of the Python Language -- 2.2 Variables and Pre-De ned Functions -- 2.2.1 Variable Types -- 2.2.2 Assigning Values to Variables -- 2.2.3 Numerical and Logical Variables -- 2.2.4 Containers -- 2.2.4.1 Lists -- 2.2.4.2 Strings -- 2.2.4.3 Tuples -- 2.2.4.4 Sets -- 2.2.4.5 Dictionaries -- 2.2.5 Variable Comparison -- 2.2.6 Type Conversion -- 2.3 Developing Python Code -- 2.3.1 Indentation -- 2.3.2 User-De ned Functions -- 2.3.3 Conditional Statements -- 2.3.4 Conditional Loops -- 2.3.5 Iterative Loop Statements -- 2.3.6 List Comprehensions -- 2.3.7 Help -- 2.4 Developing Python Programs -- 2.4.1 Data Input and Output -- 2.4.2 Reading and Writing From Files -- 2.4.3 Handling Exceptions -- 2.4.4 Modules -- 2.4.5 Putting It All Together -- 2.5 Object-Oriented Programming -- 2.5.1 De ning Classes and Creating Objects -- 2.5.2 Special Methods -- 2.5.3 Inheritance -- 2.5.4 Modularity -- 2.6 Pre-De ned Classes and Methods -- 2.6.1 Generic Methods for Containers -- 2.6.2 Methods for Lists -- 2.6.3 Methods for Strings -- 2.6.4 Methods for Sets -- 2.6.5 Methods for Dictionaries -- 2.6.6 Assigning and Copying Variables -- Bibliographical Notes and Further Reading -- Exercises and Programming Projects -- Exercises -- Programming Projects -- 3 Cellular and Molecular Biology Fundamentals -- 3.1 The Cell: The Basic Unit of Life -- 3.2 Genetic Information: Nucleic Acids -- 3.2.1 Transcription: RNA Synthesis -- 3.2.2 Translation: Protein Synthesis -- 3.3 Genes: Discrete Units of Genetic Information -- 3.3.1 Gene Structure -- 3.3.2 Regulation of Gene Expression -- 3.4 Human Genome -- 3.5 Biological Resources and Databases
Title Bioinformatics algorithms : design and implementation in Python
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