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Algorithms In Bioinformaticsa Practical Introduction 2009 Edition at Meripustak

Algorithms In Bioinformaticsa Practical Introduction 2009 Edition by Wing-Kin Sung , Taylor & Francis Ltd

Books from same Author: Wing-Kin Sung

Books from same Publisher: Taylor & Francis Ltd

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  • General Information  
    Author(s)Wing-Kin Sung
    PublisherTaylor & Francis Ltd
    ISBN9781420070330
    Pages407
    BindingHardback
    LanguageEnglish
    Publish YearDecember 2009

    Description

    Taylor & Francis Ltd Algorithms In Bioinformaticsa Practical Introduction 2009 Edition by Wing-Kin Sung

    Thoroughly Describes Biological Applications, Computational Problems, and Various Algorithmic Solutions Developed from the author's own teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied in bioinformatics. For each topic, the author clearly details the biological motivation and precisely defines the corresponding computational problems. He also includes detailed examples to illustrate each algorithm and end-of-chapter exercises for students to familiarize themselves with the topics. Supplementary material is available at http://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/This classroom-tested textbook begins with basic molecular biology concepts. It then describes ways to measure sequence similarity, presents simple applications of the suffix tree, and discusses the problem of searching sequence databases. After introducing methods for aligning multiple biological sequences and genomes, the text explores applications of the phylogenetic tree, methods for comparing phylogenetic trees, the problem of genome rearrangement, and the problem of motif finding. It also covers methods for predicting the secondary structure of RNA and for reconstructing the peptide sequence using mass spectrometry. The final chapter examines the computational problem related to population genetics. Introduction to Molecular BiologyDNA, RNA, Protein Genome, Chromosome, and Gene Replication and Mutation of DNA Central Dogma (From DNA to Protein) Post-Translation Modification (PTM) Population Genetics Basic Biotechnological Tools Brief History of BioinformaticsSequence SimilarityIntroductionGlobal Alignment ProblemLocal Alignment Semi-Global AlignmentGap Penalty Scoring FunctionSuffix TreeIntroduction Suffix Tree Simple Applications of Suffix TreeConstruction of Suffix TreeSuffix ArrayFM-IndexApproximate Searching ProblemDatabase Search Introduction Smith-Waterman Algorithm FastA BLASTVariations of the BLAST Algorithm Q-Gram Alignment Based on Suffix ARrays (QUASAR) Locality-Sensitive Hashing BWT-SW Are Existing Database Searching Methods Sensitive Enough?Multiple Sequence Alignment Introduction Formal Definition of Multiple Sequence Alignment Problem Dynamic Programming MethodCenter Star Method Progressive Alignment Method Iterative Method Genome Alignment Introduction Maximum Unique Match (MUM) Mutation Sensitive AlignmentDot Plot for Visualizing the Alignment Phylogeny ReconstructionIntroductionCharacter-Based Phylogeny Reconstruction Algorithm Distance-Based Phylogeny Reconstruction Algorithm Bootstrapping Can Tree Reconstruction Methods Infer the Correct Tree?Phylogeny Comparison Introduction Similarity MeasurementDissimilarity MeasurementsConsensus Tree ProblemGenome Rearrangement Introduction Types of Genome Rearrangements Computational Problems Sorting Unsigned Permutation by Reversals Sorting Signed Permutation by Reversals Motif Finding Introduction Identifying Binding Regions of TFs Motif Model The Motif Finding Problem Scanning for Known Motifs Statistical ApproachesCombinatorial Approaches Scoring FunctionMotif Ensemble MethodsCan Motif Finders Discover the Correct Motifs?Motif Finding Utilizing Additional InformationRNA Secondary Structure Prediction Introduction Obtaining RNA Secondary Structure Experimentally RNA Structure Prediction Based on Sequence Only Structure Prediction with the Assumption That There Is No PseudoknotNussinov Folding Algorithm ZUKER Algorithm Structure Prediction with PseudoknotsPeptide Sequencing Introduction Obtaining the Mass Spectrum of a Peptide Modeling the Mass Spectrum of a Fragmented Peptide De novo Peptide Sequencing Using Dynamic Programming De novo Sequencing Using Graph-Based Approach Peptide Sequencing via Database Search Population Genetics IntroductionHardy-Weinberg Equilibrium Linkage DisequilibriumGenotype PhasingTag SNP SelectionAssociation StudyReferencesIndexExercises appear at the end of each chapter.



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