ISBN-10:
1441946586
ISBN-13:
9781441946584
Pub. Date:
11/04/2010
Publisher:
Springer US
Bioinformatics: A Concept-Based Introduction / Edition 1

Bioinformatics: A Concept-Based Introduction / Edition 1

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Product Details

ISBN-13: 9781441946584
Publisher: Springer US
Publication date: 11/04/2010
Edition description: Softcover reprint of hardcover 1st ed. 2009
Pages: 190
Product dimensions: 6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

1 Introduction to Biological Systems Claude-Henry Volmar Nikunj Patel Amita N. Quadros Daniel Paris Venkatarajan S. Mathura Michael Mullan 1

1 Molecules of Life 1

2 Nucleic Acids: DNA Versus RNA 2

3 Understanding Proteins: Sequence-Structure-Function 4

4 Biological Systems, Signals, and Pathways 5

5 Technological Advances and Their Benefits to Biology 7

6 The Role of Bioinformatics in Big Picture 8

7 Exercises 9

References 10

2 Computer Programming Fundamentals and Concepts Deepak N. Kolippakkam Pankaj Gupta Venkatarajan S. Mathura 13

1 Purpose 13

2 Learning Objective 13

3 Perl Programming 14

3.1 Variables 14

3.2 Operators 15

3.3 Control Structures 16

3.4 Regular Expressions 17

3.5 File Handling 18

3.6 Subroutines and Functions 18

4 PHP Programming 19

4.1 Language Syntax and Data Types 19

4.2 Creating Web Interfaces 22

5 Basic RDBMS and SQL 24

5.1 Data Definition Language (DDL) 24

5.2 Data Manipulation Language (DML) 25

5.3 Data Control Language (DCL) 26

6 Web-Pointers 26

3 Introduction to Algorithms Senthilkumar Radhakrishnan Deepak Kolippakkam Venkatarajan S. Mathura 27

1 Introduction 27

1.1 Classification 27

1.2 Hypothesis Testing 28

1.3 Decision Tree 28

1.4 Clustering 29

1.5 Principal Component Analysis 29

1.6 Multidimensional Scaling 29

1.7 Regression Analysis 29

1.8 Linear Discriminant Analysis 30

1.9 Fuzzy Logic 30

1.10 Pattern Recognition 31

1.11 Bayesian Statistics 31

1.12 Neural Networks 32

1.13 Hidden Markov Model 32

1.14 Support Vector Machines 33

2 Exercises 33

3 Useful Web-Pointers 34

References 35

4 Biological Sequence Databases Meena Sakharkar Pandjassarame KangueaneVenkatarajan S. Mathura 39

1 Purpose 39

2 Learning Objective 39

3 Introduction 39

3.1 Genomic Sequence Databases - GenBank, EMBL, DDBJ 41

3.2 Protein Sequence Databases 42

3.3 Secondary Databases on Molecular Evolution 44

References 46

5 Biological Sequence Search and Analysis Venkatarajan S. Mathura 47

1 Purpose 47

2 Learning Objectives 47

3 Introduction 48

3.1 Similarity Matrices and Alignment 48

3.2 Sequence Search and Pair-Wise Alignment 50

3.3 Global Alignment Using Needleman-Wunsch Algorithm 51

3.4 Sequence Search Tools 53

3.5 Pair-Wise and Multiple-Sequence Alignment Tools 55

3.6 Sequence Motifs 57

References 61

6 Protein Structure Prediction Hongyi Zhou Yaoqi Zhou Venkatarajan S. Mathura 63

1 Introduction 63

2 Secondary Structure Prediction 65

3 Comparative Modeling 66

3.1 Steps Involved in Comparative Modeling 67

3.2 Homologous Sequence Search Using Sequence Comparison Tools 67

3.3 Identifying Remote Templates Using Fold-Recognition Methods 68

3.4 Selection of the Alignment 69

3.5 Construction of 3D Models Using Modeling Programs 69

3.6 Protein Modeling Package - MPACK 70

3.7 SP[superscript 3] - A Web-Based Structure-Prediction Tool Using Known Protein Structures as Templates 70

3.8 Modeling Servers 73

3.9 Critical Assessment of Structure Prediction 74

3.10 Objective Testing of Modeling Tools in CASP 74

References 75

7 Protein-Protein Interaction and Macromolecular Visualization Arun Ramani Venkatarajan S. Mathura Cui Zhanhua Pandjassarame Kangueane 79

1 Introduction 79

2 Experimental Methods 80

2.1 Yeast Two-Hybrid 80

2.2 Affinity Tagging 81

2.3 Computational Methods 82

2.4 Co-evolution 83

2.5 Structure Based Methods 83

3 Protein Structure Visualization 91

4 Databases 91

References 93

8 Genes, Genomics, Microarray Methods and Analysis Ghania Ait-Ghezala Venkatarajan S. Mathura 97

1 Introduction 97

2 Gene Identification and Characterization 98

2.1 Identifying Human Genes and Cloning 98

3 Microarray Experiments 102

3.1 Microarray Databases 104

3.2 Gene Annotations, Ontology, and Pathway Databases 104

References 105

9 Introduction to Proteomics Fai Poon Venkatarajan S. Mathura 107

1 Introduction 107

2 Sample Preparation 108

3 Two-Dimensional (2D) Gel Electrophoresis 108

3.1 Image Analysis and Statistical Analysis 109

3.2 In-Gel Digestion and Mass Spectrometry 109

4 Mass Spectrometry 109

4.1 Mass Spectrometry in Proteomics 110

5 Bioinformatics Applications for Identification 111

6 Conclusion 113

References 113

10 Biomedical Literature Mining Chaolin Zhang Michael Q. Zhang 115

1 Introduction 115

2 Literature Sources for Mining 117

3 Recognition of Biological Terms 118

3.1 Gene/Protein Name Recognition 119

3.2 Removing Gene/Protein Name Ambiguities 120

3.3 Collecting Other Keywords 120

4 Mining Biological Relationships 121

4.1 Detecting Gene Interactions by Co-occurrence 121

4.2 Inferring Implicit Relationships 122

4.3 Identifying Sub-networks of Communities 123

4.4 Evaluating Functional Coherence of Gene Group 124

5 Acknowledgments 124

References 125

11 Computational Immunology: HLA-Peptide Binding Prediction Pandjassarame Kangueane Bing Zhao Meena K. Sakharkar 129

1 Background 129

2 HLA Molecules 131

3 HLA Binding Peptide Based Methods 132

3.1 Sequence Based Prediction Models 133

3.2 Molecular Structure Based Predictions 143

4 Conclusion 150

References 151

12 Bioinformatics Application: Eukaryotic Gene Count and Evolution Meena K. Sakharkar Pandjassarame Kangueane 155

1 Introduction 155

2 Methodology 156

2.1 Identification of SEG 156

2.2 Identification of MEG 156

2.3 Pseudogenes 157

2.4 Caveats 157

2.5 Total Genes 158

3 Results and Discussion 158

3.1 Utility of SEG and MEG Sequences to the Study of Evolution 158

3.2 Selection of SEG and MEG in Different Eukaryotic Genomes 158

3.3 Mechanism of SEG Origin 160

4 Conclusion 161

References 162

13 Bioinformatics Application: Predicting Protein Subcellular Localization by Applying Machine Learning Pingzhao Hu Clement Chung Hui Jiang Andrew Emili 163

1 Introduction 163

2 Methods 165

2.1 Data Sets and Preprocessing 165

2.2 Learning Algorithm 166

2.3 Evaluating Performance of the Learning Algorithm 167

2.4 Strategy for Multi-class/Multi-label Classification 167

2.5 Optimal Sampling Methods for Imbalanced Data Sets 168

2.6 Algorithm of Asymmetric Bagging Strategy 169

3 Results 170

4 Discussion 172

References 172

14 Bioinformatics Analysis: Gene Fusion Meena Kishore Sakharkar Yiting Yu Pandjassarame Kangueane 175

1 Introduction 175

2 Identification of Fusion Proteins 176

2.1 Human Fusion Proteins Mimicking Bacterial Operons 177

2.2 Human Fusion Proteins Simulating Bacterial Subunit Interfaces 177

2.3 Fusion Proteins Exhibiting Multiple Functions 177

2.4 Fusion Proteins Showing Alternative Splicing 178

3 Remarks on Fusion Proteins 178

References 180

Index 183

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