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Features
Presents a comprehensive overview of principles and methods in bioinformatics
Covers numerous applications of algorithms in bioinformatics
Discusses the practical issues and actual performance of using various methods with real biological data
Assumes no prior knowledge of molecular biology
Offers PowerPoint slides and other supplementary material on the author's website
Solutions manual available for qualifying instructors

Summary
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.



Table of Contents
Introduction to Molecular Biology

DNA, 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 Bioinformatics

Sequence Similarity

Introduction

Global Alignment Problem

Local Alignment

Semi-Global Alignment

Gap Penalty

Scoring Function

Suffix Tree

Introduction

Suffix Tree

Simple Applications of Suffix Tree

Construction of Suffix Tree

Suffix Array

FM-Index

Approximate Searching Problem

Database Search

Introduction

Smith-Waterman Algorithm

FastA

BLAST

Variations 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 Method

Center Star Method

Progressive Alignment Method

Iterative Method

Genome Alignment

Introduction

Maximum Unique Match (MUM)

Mutation Sensitive Alignment

Dot Plot for Visualizing the Alignment

Phylogeny Reconstruction

Introduction

Character-Based Phylogeny Reconstruction Algorithm

Distance-Based Phylogeny Reconstruction Algorithm

Bootstrapping

Can Tree Reconstruction Methods Infer the Correct Tree?

Phylogeny Comparison

Introduction

Similarity Measurement

Dissimilarity Measurements

Consensus Tree Problem

Genome 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 Approaches

Combinatorial Approaches

Scoring Function

Motif Ensemble Methods

Can Motif Finders Discover the Correct Motifs?

Motif Finding Utilizing Additional Information

RNA 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 Pseudoknot

Nussinov Folding Algorithm

ZUKER Algorithm

Structure Prediction with Pseudoknots

Peptide 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

Introduction

Hardy-Weinberg Equilibrium

Linkage Disequilibrium

Genotype Phasing

Tag SNP Selection

Association Study

References

Index

Exercises appear at the end of each chapter.