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生物信息学-BIOINFORMATICS: A CONCEPT-BASED INTRODUCTION
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Scientific disciplines evolve and mature into different areas of specialization to accommodate new knowledge and methods that are being developed by the research community. The last decade has seen a dramatic change in most fields and the information technology has revolutionized several fields. Bioinformatics is the perfect marriage between computer science and advanced biology. Complex biological processes, macromolecular components and their functional interplay define the basis of living cells. Biological experiments that aim to reveal the complexity of cellular systems and biomolecular functions produce huge volumes of data or information that needs to be efficiently handled for tangible results. Exponential increase in genome sequences, protein sequences, protein interactions and biological networks/pathways information has created a demand for efficient information handling. This led to the birth of the field of Bioinformatics that aims to handle biological information using computational methods and algorithms. Bioinformatics is evolving into a mature field with an everincreasing participation from the scientific community. The past five years have seen a rapid increase in the number of scientific journals in this field. It is impossible to include all the topics of Bioinformatics in a book and still cater to the needs of newcomers attracted to this field. This is an introductory book that provides a balance between computational methods and biological information. Instead of delving in depth, for each topic we provide a broad but necessary content that will benefit readers with different levels of expertise. 1 Introduction to Biological Systems........................................................1 Claude-Henry Volmar, Nikunj Patel, Amita N. Quadros, Daniel Paris, Venkatarajan S. Mathura and Michael Mullan 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 ....................13 Deepak N. Kolippakkam, Pankaj Gupta and Venkatarajan S. Mathura 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 xii Contents 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 ..................................................................27 Senthilkumar Radhakrishnan, Deepak Kolippakkam and Venkatarajan S. Mathura 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 ............................................................39 Meena Sakharkar, Pandjassarame Kangueane and Venkatarajan S. Mathura 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 Contents xiii 5 Biological Sequence Search and Analysis...........................................47 Venkatarajan S. Mathura 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................................................................63 Hongyi Zhou, Yaoqi Zhou and Venkatarajan S. Mathura 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 SP3 – 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...... 79 Arun Ramani, Venkatarajan S. Mathura, Cui Zhanhua and Pandjassarame Kangueane 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 xiv Contents 2.5 Structure Based Methods .............................................................. 83 3. Protein Structure Visualization........................................................... 91 4. Databases ............................................................................................ 91 References............................................................................................... 93 8 Genes, Genomics, Microarray Methods and Analysis ...................... 97 Ghania Ait-Ghezala and Venkatarajan S. Mathura 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 ................................................................107 Fai Poon and Venkatarajan S. Mathura 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 ...........................................................115 Chaolin Zhang and Michael Q. Zhang 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 References............................................................................................. 125 5. Acknowledgments ............................................................................ 124 Contents xv 11 Computational Immunology: HLA-Peptide Binding Prediction....129 Pandjassarame Kangueane, Bing Zhao and Meena K. Sakharkar 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 ...........................................................................155 Meena K. Sakharkar and Pandjassarame Kangueane 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 ...................................163 Pingzhao Hu, Clement Chung, Hui Jiang and Andrew Emili 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 xvi Contents 14 Bioinformatics Analysis: Gene Fusion..............................................175 Meena Kishore Sakharkar, Yiting Yu 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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