Bioinformatics for Diagnosis, Prognosis and Treatment of Complex Diseases (Translational Bioinformatics Book 4)

The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics. Contents 6 Part IBioinformatics for Complex Diseases: The Basics 8 1 Molecular Diagnostic Techniques 9 Abstract 9 1.1...Introduction 9 1.2...Polymerase Chain Reaction and Real-Time PCR 10 1.2.1 PCR 10 1.2.2 Real-Time PCR 11 1.2.3 FISH 13 1.3...Mutation Analysis 15 References 20 2 Identifying Biomarkers with Differential Analysis 23 Abstract 23 2.1...Introduction 24 2.2...Differential Analysis in Biology 25 2.3...Gene Biomarkers 26 2.4...Gene Set Biomarkers 28 2.5...Pathway Biomarkers 29 2.6...Network Biomarkers 30 2.7...Conclusions and Perspective 33 References 34 3 Identifying Driver Mutations in Cancer 38 Abstract 38 3.1...Introduction 38 3.1.1 What is Driver Mutation? 38 3.1.2 Properties of Driver Mutations 40 3.1.3 Evolutionary Model of Cancer 40 3.1.4 Types of Cancer Genes 41 3.1.5 Types of Genetic Alterations in Cancer 42 3.2...Overview of Computational Methods to Identify Driver Mutations 43 3.2.1 Challenges for Driver Mutation Identification 43 3.2.2 Resources Available for Driver Mutation Identification 44 3.2.3 Summary of Different Algorithms for Driver Mutation Identification 44 3.3...Sequence-Based Approaches 45 3.3.1 MutationAssessor 46 3.3.2 TransFic 47 3.3.3 SNPs3D 48 3.4...Machine Learning-Based Methods 49 3.4.1 CHASM 49 3.4.2 CRAVAT 50 3.4.3 Polyphen2 and SNAP 50 3.5...Frequency-Based Methods 51 3.5.1 MutSig 51 3.5.2 MutSigCV 52 3.5.3 ActiveDriver and MuSiC 52 3.6...Pathway-Based Methods 53 3.6.1 MEMo 53 3.6.2 HotNet and Dendrix 54 3.6.3 DriverNet 55 3.6.4 PARADIGM-Shift 56 3.7...Discussion 57 References 57 4 Biomarker Discovery with Text Mining and Literature Based Discovery 62 Abstract 62 4.1...Introduction to Biomedical Text Mining 62 4.2...Tasks and Phases of Biomedical Text Mining 63 4.2.1 Information Retrieval 63 4.2.2 Named Entity Recognition 64 4.2.3 Relation Extraction 65 4.2.4 Knowledge Discovery 66 4.2.5 Hypothesis Generation 66 4.3...Workflow of Text Mining Based Systems Biology Research 67 4.4...MicroRNAs Discovery with Biomedical Text Mining 69 4.5...Biomarker Identification Using Text Mining from PubMed 70 4.6...Data Sets and Tools for Biomedical Text Mining 72 4.6.1 Named Entity Recognition 72 4.6.2 Synonym and Abbreviation Recognition 72 4.6.3 Relation Extraction 72 4.7...Challenges and Future Work 75 4.8...Conclusions 78 References 79 5 Protein Binding Interfaces and Their Binding Hot Spot Prediction: A Survey 84 Abstract 84 5.1...Proteins: An Elementary Introduction 84 5.1.1 A Definition for Proteins 85 5.1.2 Database of Protein 3D Structural Data 87 5.2...Protein Binding Interfaces 88 5.2.1 Diversity of Protein Interactions 89 5.2.2 Binding Interfaces in Protein Interactions 90 5.3...Uneven Distribution of Binding Free Energy and Binding Hot Spots in Protein Interfaces 93 5.4...Definition of Protein Interfaces: An Intractable but Fundamental Research Problem 94 5.5...Current Research on Binding Interfaces 97 5.5.1 Computational Structural Analysis of Protein Interfaces 97 5.5.2 Limitations in the Current Approaches to Protein Interfaces Analysis 98 5.6...Modeling and Prediction of Binding Hot Spots 100 5.6.1 Modeling Binding Hot Spots 100 5.6.2 Estimating Delta Delta G of Binding Hot Spots 101 5.6.3 Identifying Binding Hot Spots Qualitatively 102 5.6.3.1 Hot Spot Identification Based on Quaternary Structures 102 5.6.3.2 Hot Spot Identification Based on Tertiary or Primary Structures 104 5.6.4 Databases for Binding Hot Spots 105 5.7...Conclusion 106 Acknowledgments 107 References 107 Part IINetwork Based Diagnosis of Complex Diseases 112 6 Systems Biology Studies of Gene Network and Cell Signaling Pathway in Cancer Research 113 Abstract 113 6.1...Introduction 113 6.2...Top--Down Approaches 115 6.2.1 Probabilistic Graphical Models 115 6.2.2 Regression Method 116 6.2.3 Information Theory 117 6.2.4 Network Topology 117 6.2.5 Other Methods 118 6.3...Bottom--Up Approaches 118 6.3.1 Boolean Network Model 118 6.3.2 Deterministic Differential Equations 119 6.3.3 Discrete Stochastic Models 120 6.3.4 Continuous Stochastic Models 121 6.3.5 Reverse-Engineering of Dynamic Models 121 6.4...Case Study of Top--Down Approaches 122 6.5...Case Study of Bottom--Up Methods 123 6.6...Conclusion 128 Acknowledgments 129 References 129 7 A Network Systems Approach to Identify Functional Epigenetic Drivers in Cancer 134 Abstract 134 7.1...Introduction 135 7.1.1 General Background, Aims and Chapter Organisation 135 7.1.2 Epigenomics Links Ageing, Stem Cell Biology and Cancer 136 7.1.3 Network Biomarkers 137 7.2...A Systems Approach to Epigenomics 139 7.2.1 The Human Interactome Exhibits DNA Methylation Correlation Modularity 139 7.3...Differential Methylation Interactome Hotspots 141 7.3.1 General Considerations 141 7.3.2 Module Detection Using a Spin-Glass Algorithm 142 7.3.3 The Importance of Module Size 143 7.3.4 Differential Methylation Hotspots Associated with Ageing 145 7.4...Functional Epigenetic Driver Modules 145 7.4.1 The Functional Epigenetic Module Algorithm 147 7.4.2 Application to Endometrial Cancer: The HAND2 Module 147 7.5...Conclusions 151 Acknowledgments 151 References 151 8 Identification of Cancer MicroRNA Biomarkers Based on miRNA--mRNA Network 156 Abstract 156 8.1...Introduction 157 8.2...Advances of miRNA Expression Profiling Methods 158 8.2.1 High-Throughput Screening Approaches 158 8.2.2 Low-Throughput Detection Methods 159 8.3...Traditional Approaches for Cancer miRNA Biomarker Discovery 161 8.4...The Reconstruction of miRNA-mRNA Network 161 8.5...Computational Based Approaches for Individual miRNA Biomarker Discovery 162 8.5.1 Based on Gene Expression Data and miRNA--mRNA Network Information 162 8.5.2 Based on miRNA and Gene Expression Profiles and miRNA--mRNA Network Information 163 8.6...Computational Based Approaches for miRNA Network Biomarker Discovery 164 8.6.1 The Discovery of Cancer-Related miRNA--mRNA Regulatory Modules 164 8.6.2 The Discovery of Cancer-Related MicroRNA Network Biomarkers 164 8.7...Databases on Potential Cancer miRNA Biomarkers 166 8.8...Future Directions 167 8.9...Conclusions 167 References 168 Part IIIApplications in Detection and Treatment of Complex Diseases 171 9 Ubiquitin and Ubiquitin-Like Conjugations in Complex Diseases: A Computational Perspective 172 Abstract 172 9.1...Introduction 173 9.2...Advances in High-Throughput Proteomic Analysis of Ub/UBL Conjugations 175 9.3...Data Resources for Ub/UBL Conjugations 176 9.4...Prediction of Ub/UBL Conjugation Sites 178 9.5...Computational Analysis of Disease-Associated Ub/UBL Conjugations Provides Potential Biomarkers and Drug Targets 180 9.6...Personal Perspectives on Further Computational Analysis of Ub/UBL Conjugations 182 9.7...Conclusion 183 Acknowledgments 183 References 183 10 Identification of Biomarkers for Pharmacological Activity 189 Abstract 189 10.1...Introduction 189 10.2...Pharmacogenomic Biomarkers 190 10.2.1 Genome-Wide Association Study 192 10.2.2 Gene Expression Analysis 192 10.2.3 Next-Generation Sequencing 193 10.3...Pharmacoproteomic Biomarkers 194 10.4...Pharmacometabolomic Biomarkers 195 10.5...Examples of Biomarkers for Pharmacological Activity 196 10.5.1 Cancer Biomarkers 196 10.5.2 Biomarkers for Other Diseases 199 10.6...Bioinformatics for Biomarkers 199 10.6.1 FDA Labels 199 10.6.2 PharmGKB 200 10.6.3 OmniBioMarker 200 10.7...Conclusion 201 Acknowledgments 201 References 201 11 Network Biomarkers for Diagnosis and Prognosis of Human Prostate Cancer 206 Abstract 206 11.1...Introduction 206 11.2...Current Prostate Cancer Biomarker Discovered by Genomics and Proteomics Technologies 207 11.3...Pathway-Level Analysis of Prostate Cancer 208 11.4...Network-Based Biomarker Discovery 211 11.5...Research Pipelines for Network Biomarker Identification 212 11.5.1 Searching for PPI Subnetwork with Discriminate Potential 212 11.5.2 Scoring Subnetworks 214 11.5.3 Training Classifiers 215 11.5.4 Performance Evaluation 215 11.6...Network-Based Biomarkers in Cancers 215 11.7...Network-Based Biomarkers for Prostate Cancer 216 11.8...Conclusions 217 References 217 The Book Introduces The Bioinformatics Tools, Databases And Strategies For The Translational Research, Focuses On The Biomarker Discovery Based On Integrative Data Analysis And Systems Biological Network Reconstruction. With The Coming Of Personal Genomics Era, The Biomedical Data Will Be Accumulated Fast And Then It Will Become Reality For The Personalized And Accurate Diagnosis, Prognosis, And Treatment Of Complex Diseases. The Book Covers Both State Of The Art Of Bioinformatics Methodologies And The Examples For The Identification Of Simple Or Network Biomarkers. In Addition, Bioinformatics Software Tools And Scripts Are Provided To The Practical Application In The Study Of Complex Diseases. The Present State, The Future Challenges And Perspectives Were Discussed. The Book Is Written For Biologists, Biomedical Informatics Scientists, And Clinicians, Et Cetera. Bioinformatics For Complex Diseases: The Basics -- Network Based Diagnosis Of Complex Diseases -- Applications In Detection And Treatment Of Complex Diseases. Editor, Bairong Shen. Includes Bibliographical References. Mode Of Access: World Wide Web. Front Matter....Pages i-vi Front Matter....Pages 1-1 Molecular Diagnostic Techniques....Pages 3-16 Identifying Biomarkers with Differential Analysis....Pages 17-31 Identifying Driver Mutations in Cancer....Pages 33-56 Biomarker Discovery with Text Mining and Literature Based Discovery....Pages 57-78 Protein Binding Interfaces and Their Binding Hot Spot Prediction: A Survey....Pages 79-106 Front Matter....Pages 107-107 Systems Biology Studies of Gene Network and Cell Signaling Pathway in Cancer Research....Pages 109-129 A Network Systems Approach to Identify Functional Epigenetic Drivers in Cancer....Pages 131-152 Identification of Cancer MicroRNA Biomarkers Based on miRNA–mRNA Network....Pages 153-167 Front Matter....Pages 169-169 Ubiquitin and Ubiquitin-Like Conjugations in Complex Diseases: A Computational Perspective....Pages 171-187 Identification of Biomarkers for Pharmacological Activity....Pages 189-205 Network Biomarkers for Diagnosis and Prognosis of Human Prostate Cancer....Pages 207-220