Advances in Bioinformatics

Description

This book presents the latest developments in bioinformatics, highlighting the importance of bioinformatics in genomics, transcriptomics, metabolism and cheminformatics analysis, as well as in drug discovery and development. It covers tools, data mining and analysis, protein analysis, computational vaccine, and drug design. Covering cheminformatics, computational evolutionary biology and the role of next-generation sequencing and neural network analysis, it also discusses the use of bioinformatics tools in the development of precision medicine. This book offers a valuable source of information for not only beginners in bioinformatics, but also for students, researchers, scientists, clinicians, practitioners, policymakers, and stakeholders who are interested in harnessing the potential of bioinformatics in many areas.  Foreword Preface Acknowledgement Contents About the Editors 1: An Introduction and Applications of Bioinformatics 1.1 Introduction 1.2 Applications of Bioinformatics 1.2.1 DNA Sequence and Analysis 1.2.2 Genome Sequencing 1.2.3 Genome Annotation and Analysis 1.3 Computational Evolutionary Biology 1.4 Comparative Genomics 1.4.1 Pan-genomics 1.4.2 Genetics of Disease 1.4.3 Analysis of Gene Mutations in Cancer 1.5 Bioinformatics in Gene and Protein Expression Analysis 1.5.1 Analysis of Gene Expression 1.5.2 Analysis of Protein Expression 1.5.3 Analysis of Gene Regulation 1.6 Structural Bioinformatics 1.7 Immunoinformatics for Vaccine Design 1.8 Conclusions and Future Perspective References 2: Bioinformatics Tools and Software 2.1 Introduction 2.2 Importance of Bioinformatics 2.3 Tools Used in Bioinformatics 2.3.1 Sequence Submission Tools 2.3.1.1 BanqIt 2.3.1.2 SPIN 2.3.1.3 WEBIN 2.3.1.4 Sequin 2.3.1.5 SAKURA 2.3.2 Sequence Retrieval Tools 2.3.2.1 Entrez 2.3.2.2 SRS 2.3.2.3 Getentry 2.3.3 Structure Submission Tools 2.3.3.1 ADIT 2.3.3.2 pdb_extract 2.3.3.3 AutoDep 2.3.3.4 EMDep 2.3.3.5 OneDep 2.3.4 Sequence Analysis Tools 2.3.4.1 BLAST Types of BLAST Statistical Significance BLAST Output Format 2.3.4.2 CLUSTAL W Output Format 2.3.4.3 CLUSTAL X 2.3.5 Structure Prediction Tools 2.3.5.1 SWISS-MODEL 2.3.5.2 Modeller 2.3.5.3 JPred 2.3.5.4 3D-Jigsaw 2.3.5.5 ModBase 2.4 Concluding Remarks References Online Resources 3: Role of Bioinformatics in Biological Sciences 3.1 Introduction 3.2 Role of Bioinformatics 3.2.1 Genomics 3.2.1.1 Structural Genomics 3.2.1.2 Functional Genomics 3.2.1.3 Nutritional Genomics 3.2.2 Transcriptomics 3.2.3 Proteomics 3.2.4 Metabolomics 3.2.5 Chemoinformatics 3.2.6 Molecular Phylogeny 3.2.7 Systems Biology 3.2.8 Synthetic Biology 3.3 Research Areas of Bioinformatics 3.3.1 Development of Biological Database 3.3.2 Sequence Analysis 3.3.3 Genome Analysis 3.3.4 Three-dimensional (3D) Structure Prediction 3.3.5 Clinical Applications 3.3.6 Drug Discovery Research 3.3.7 Mathematical Modeling of Metabolic Processes 3.4 Concluding Remarks References Online Resources 4: Protein Analysis: From Sequence to Structure 4.1 Introduction 4.2 Protein Structure Overview 4.2.1 Primary Structure 4.2.2 Secondary Structure 4.2.2.1 Alpha(α) Helix 4.2.2.2 The β-strand 4.2.2.3 310 Helices 4.2.2.4 β-turns 4.2.3 Tertiary Structure 4.2.4 Quaternary Structure 4.2.5 Domains, Motifs, and Folds 4.2.5.1 Domain 4.2.5.2 Motifs 4.2.5.3 Fold 4.3 Classification of Proteins Based on Protein Folding Patterns 4.3.1 CATH (Class, Architecture, Topology, Homology) 4.3.2 SCOP (Structural Classification of Proteins) 4.4 Commonly Used Databases to Retrieve Protein Sequence and Structure Information 4.4.1 Commonly Used Protein Sequence Databases 4.4.1.1 Protein Information Resource (PIR) 4.4.2 Structure Database 4.4.2.1 PDB 4.4.3 Composite Databases 4.4.3.1 Swiss-Prot 4.4.3.2 PROSITE 4.4.3.3 PRINT 4.4.3.4 BRENDA (BRaunschweig ENzyme DAtabase) 4.4.3.5 Pfam 4.5 Protein Sequence Analysis 4.5.1 Protein Sequence Alignment 4.5.1.1 Clustal 4.5.1.2 Sequence Alignment in Database Searching BLAST 4.5.2 Physicochemical Parameters from Sequence Analysis 4.5.2.1 ProtParam Molecular Weight Theoretical PI Grand Average of Hydropathicity (GRAVY) Half-life Instability Index Extinction Coefficient 4.5.2.2 Protein-Sol 4.6 Protein Structure Prediction 4.6.1 Secondary Structure Prediction 4.6.1.1 Chou-Fasman Method 4.6.1.2 (Garnier-Osguthorpe-Robson) GOR Method 4.6.1.3 Neural Network-Based Method 4.6.2 Protein Tertiary Structure Prediction 4.6.2.1 Template-based Method for Predicting Tertiary Structure of Proteins SWISS-MODEL Modeller I-TASSER 4.6.2.2 Template Free Method for Predicting Tertiary Structure of Proteins 4.6.3 CASP 4.7 Evaluation, Refinement, and Analysis of Predicted Protein Structure 4.7.1 Evaluation of Predicted Structure 4.7.2 Structure Refinement 4.7.3 Structure Analysis 4.7.3.1 Molecular Dynamics Simulation 4.8 Protein Interaction Studies Using In Silico Methods 4.8.1 Protein-Protein Interaction (PPI) 4.8.2 Protein DNA Interaction 4.8.3 Protein-Carbohydrate Interaction 4.9 Applications of Protein Sequence and Structure Analysis in Drug Discovery 4.10 Conclusion References 5: Computational Evolutionary Biology 5.1 Introduction 5.2 Substitution Model 5.2.1 Nucleotide Substitution Model 5.2.2 Amino Acid Substitution Model 5.3 Molecular Clock Estimation 5.4 Tools for Genome Biology and Evolution 5.5 Insights Into Human Evolution 5.6 Role in Viral Evolution 5.7 Conclusion References 6: Web-Based Bioinformatics Approach Towards Analysis of Regulatory Sequences 6.1 Introduction 6.2 Why Care About Regulatory Sequences? 6.3 The Marriage of Omics and Bioinformatics 6.4 Web-Based Tools as Powerful Assets to Analyse Regulatory Sequences 6.5 Discovery of Over-Represented Oligonucleotides (motifs) in Regulatory Sequences 6.6 Regulatory Sequence Analysis Tools (RSAT) 6.7 Future Perspectives References 7: An Overview of Bioinformatics Resources for SNP Analysis 7.1 Introduction 7.1.1 Types of SNPs 7.1.2 Applications of SNPs 7.1.2.1 Strain Genotyping 7.1.2.2 Selective Breeding of Plants and Animals 7.1.2.3 Forensic Analysis 7.1.2.4 Personalized Medicine 7.2 SNP Discovery and Identification 7.3 SNP Data Resources 7.4 Functional Interpretation of SNPs 7.4.1 Sequence-Based Analysis 7.4.2 Structure-Based Analysis 7.5 Future Perspectives References 8: Vaccine Design and Immunoinformatics 8.1 Introduction 8.2 Immunoinformatics in Vaccine Discovery and Infectious Diseases 8.3 Computational Databases for Prediction of T Cell Epitopes 8.4 Computational Databases for Prediction of B Cell Epitopes 8.5 Prediction of T Cell Epitope Modeling 8.6 Multi-Epitope Vaccine Design as a Promising Approach 8.7 Docking and Simulation of Peptides to Enhance Vaccine Design Approach 8.8 Conclusion References 9: Computer-Aided Drug Designing 9.1 Introduction 9.1.1 Drug and Drug Designing 9.1.2 Computer-Aided Drug Discovery 9.2 Approaches to Drug Designing 9.2.1 Structure-Based Drug Design (SBDD) 9.2.1.1 Structure-Based Virtual Screening 9.2.1.2 Structure-Based Lead Optimization (In silico) 9.2.2 Ligand-Based Drug Design 9.3 Introduction and Principal of the QSAR 9.3.1 Historical Progress and Development of QSAR 9.3.2 Statistical Tools Applied for QSAR Model Development and Validation 9.3.3 Molecular Descriptors applied in QSAR 9.3.4 Approaches of QSAR 9.3.4.1 2D-QSAR Techniques Hansch Analysis Free-Wilson Analysis 9.3.4.2 3D-QSAR Comparative Molecular Field Analysis (COMFA) Drawbacks and Limitations of CoMFA Application Comparative Molecular Similarity Indices (COMSIA) 9.4 The Concept of Pharmacophore Mapping 9.4.1 Pharmacophore-Model-Based Virtual Screening 9.4.1.1 Ligand-Based Pharmacophore Modeling 9.4.1.2 Structure-Based Pharmacophore Modeling 9.5 ADME-Tox (Absorption, Distribution, Metabolism, Excretion-Toxicity) 9.6 Applications of CADD Approach in Lead Discovery 9.7 Conclusion References 10: Chemoinformatics and QSAR 10.1 Introduction 10.2 Overview of QSAR 10.3 QSAR Methods 10.3.1 Forward Selection (FS) Method 10.3.2 Backward Elimination (BE) Method 10.3.3 Stepwise Selection (SS) Method 10.3.4 Variable Selection and Modeling Method 10.3.5 Leaps-and-Bounds Regression 10.3.6 QSAR Modeling and Development 10.3.7 Internal Model Validation 10.3.8 External Model Validation 10.3.9 Randomization Test 10.4 Molecular Descriptors 10.4.1 2D QSAR Descriptors 10.4.2 Constitutional Descriptors 10.4.3 Electrostatic and Quantum-Chemical Descriptors 10.4.4 Topological Descriptors 10.4.5 Geometrical Descriptors 10.5 3D QSAR Descriptors 10.6 Alignment-Dependent 3D QSAR Descriptors 10.6.1 Comparative Molecular Field Analysis (CoMFA) 10.6.2 Comparative Molecular Similarity Indices Analysis (CoMSIA) 10.6.3 Weighted Holistic Invariant Molecular Descriptors (WHIM) 10.6.4 VolSurf 10.6.5 Grid-Independent Descriptors (GRIND) 10.7 QSAR Based Screening 10.8 QSAR Modeling and Validation 10.9 Decoys Selection 10.9.1 Physicochemical Filters to the Decoy Compounds Selection 10.9.2 Benchmarking Database Biases 10.9.3 Structure-Based Method 10.9.4 Ligand-Based Method 10.10 Inverse-QSPR/QSAR 10.10.1 Unguided Generation of Molecules 10.11 Workflow and Application of QSAR Modeling in Virtual Screening 10.12 Future Directions and Conclusion References 11: Computational Genomics 11.1 DNA Decoding 11.1.1 Sequencing Platforms 11.1.2 Whole-Genome Sequencing and Whole-Transcriptome Sequencing 11.2 Sequence Alignment 11.2.1 Biological Sequence Alignment 11.2.1.1 Pairwise Sequence Alignment and Dynamic Programming Global Alignment Local Alignment 11.2.1.2 Multiple Sequence Alignment (MSA) 11.3 Genome Assembly and Annotation 11.3.1 Reference-Based Assembly 11.3.1.1 Mapping Algorithms and Tools 11.3.1.2 Advantages and Disadvantages 11.3.2 De Novo Assembly 11.3.3 Hybrid Assembly 11.3.4 Gene Prediction and Annotation 11.4 Biological Interaction Network 11.4.1 Biological Network Properties 11.4.2 Types of Biological Networks 11.4.2.1 Metabolic Networks 11.4.2.2 Signaling Network 11.4.2.3 Gene Regulation Network 11.4.2.4 Protein-Protein Interaction Network 11.4.2.5 Biological Co-Expression Network References 12: A Guide to RNAseq Data Analysis Using Bioinformatics Approaches 12.1 Introduction 12.2 Platforms Available for Sequencing 12.2.1 SOLiD 12.2.2 Ion Torrent Semiconductor Sequencing 12.2.3 Illumina Sequencing Technology 12.3 Quality Check and Pre-Processing of Reads 12.3.1 Formats Available for Storage of Raw Data 12.3.2 Quality Check Using Available Softwares and Tools 12.3.3 Pre-Processing of Data 12.4 Assembling Reads to Reference Genome/Transcriptome 12.4.1 Alignment of Reads 12.4.2 Reference Guided/de Novo Assembly 12.4.3 Quality Check (QC) of Assembled Reads 12.5 Expression Quantification and Differential Expression 12.6 Annotation 12.6.1 Functional Annotation 12.6.2 Pathway Analysis 12.6.3 Gene Ontology (GO) Analysis 12.7 Other RNAseq Applications 12.7.1 Single Cell RNAseq 12.7.2 Small RNA Sequencing 12.8 Concluding Remarks References 13: Computational Metabolomics 13.1 Introduction 13.2 Factors Influencing Variations and Redundancy in Metabolomics Data 13.3 NMR Spectroscopy for Metabolite Identification 13.3.1 Homonuclear Experiments 13.3.2 Heteronuclear Experiences 13.4 Organization of 1D and 2D Dataset and their Format 13.4.1 GSim: Bruker FID File into ASCII File Converter 13.4.2 Converting ASCII File into CSV File 13.5 Data Preprocessing Methods 13.5.1 Fourier Transformation of FID 13.5.2 Phase Correction or Phasing 13.5.3 Noise Filtering 13.5.3.1 Baseline Correction 13.5.4 Peak Alignment 13.5.5 Binning/Bucketing 13.5.6 Normalization 13.6 Data Processing Tools/R-Packages 13.6.1 NMRS 13.6.2 ChemoSpec 13.6.3 Speq 13.6.4 BATMAN (Bayesian AuTomated Metabolite Analyzer for NMR) 13.6.5 NMRPipe 13.6.6 rNMR 13.7 NMR Spectral Libraries for Metabolite Identification 13.7.1 Madison Metabolomics Consortium Database (MMCD) 13.7.2 Biological Magnetic Resonance Data Bank (BMRDB) 13.7.3 NMRShiftDB 13.8 Conclusion References 14: Next Generation Sequencing 14.1 Introduction 14.2 Fragmentation 14.3 Adapter Ligation 14.4 Sequencing 14.5 Data Analysis 14.5.1 Whole Genome Sequencing 14.5.2 Whole Exome Sequencing 14.5.3 Whole Transcriptome Shotgun Sequencing 14.5.4 Metagenome Sequencing 14.5.5 Machine Learning in NGS 14.5.6 ML and NGS Data Analysis 14.6 Tools for NGS Data Analysis 14.6.1 Current Challenges of NGS 14.7 Conclusions and Future Perspectives References 15: Bioinformatics in Personalized Medicine 15.1 Introduction 15.2 Significance of Personal Medicine and Bioinformatics 15.3 Application of Bioinformatics in Personal Medicines and Vaccines 15.4 Advantages and Disadvantages of PM 15.5 Bioinformatics Prerequisites Challenges for Personal Medicine Design 15.6 Advanced Methods Involved in Personalized Medicine Designing 15.7 Conclusions References 16: Bioinformatics Tools for Gene and Genome Annotation Analysis of Microbes for Synthetic Biology and Cancer Biology Applicat... 16.1 Introduction 16.2 Current Status of Gene Annotation (Automated and Manual) in Microbes 16.3 Gene Features on the Prokaryotic Genome 16.4 Gene Ontology and Community Annotation in Cancer Biology 16.5 Application of Microbial Genomics in Cancer Biology 16.6 Application of Microbes for Cancer Treatment and Cancer Precision Medicine 16.7 Conclusion References 17: Bioinformatics for Human Microbiome 17.1 Introduction 17.2 Overview of Sequencing Methods and Bioinformatics Analysis 17.2.1 Pre-Processing of Sequencing Data 17.2.2 Metataxonomics 17.2.3 Metagenomics 17.2.4 Metatranscriptomics 17.2.5 Databases for Microbial Taxonomic Assignments 17.3 Downstream Analysis 17.3.1 Microbial Taxonomic Analysis 17.3.2 Microbial Functional Analysis 17.4 Integrating Multiomic Data of Microbiome Samples 17.5 Pre-Designed Pipelines and Web-Analysis Platforms 17.6 Challenges and Best Practices for Microbiome Analysis 17.7 Application of Human Microbiome Research in Human Diseases References 18: Neural Network Analysis 18.1 Introduction 18.2 Biochemistry and Bioinformatics Background 18.3 Neural Networks and Its Types 18.4 Application of Neural Networks 18.4.1 Prediction of Structure for Proteins 18.4.2 Binding Patterns and Epitope Selection: Immuno-Informatics Application 18.4.3 Role in Genomics and Transcriptomics 18.5 Conclusion References 19: Role of Bioinformatics in MicroRNA Analysis 19.1 Introduction 19.2 microRNA Database 19.2.1 miRBase 19.3 Tools for miRNA Target Prediction 19.3.1 psRNATarget 19.3.2 RNA Hybrid 19.3.3 MiR Scan 19.4 Concluding Remarks References 20: Bioinformatics for Image Processing 20.1 Introduction 20.2 Medical Imaging Techniques 20.2.1 X-Ray Radiography 20.2.1.1 X-Ray Radiography Advantage 20.2.1.2 Risks from X-Ray Radiography 20.2.1.3 Applications of X-Ray Radiography 20.2.2 Computed Tomography (CT) 20.2.2.1 Computed Tomography Advantages 20.2.2.2 Computed Tomography Risks 20.2.2.3 Computed Tomography Medical applications 20.2.3 Magnetic Resonance Imaging (MRI) 20.2.3.1 MRI Advantages 20.2.3.2 MRI Risks 20.2.3.3 MRI Medical Applications 20.2.4 Ultrasonography 20.2.4.1 Advantages of Ultrasonography 20.2.4.2 Risks in Ultrasonography 20.2.4.3 Ultrasonography Health applications 20.2.5 Elastography 20.2.5.1 Benefits from Elastography 20.2.5.2 Elastography Risks 20.2.5.3 Medical Applications for Elastography 20.2.6 Optical Imaging 20.2.6.1 Benefits of Optical Imaging 20.2.6.2 Risks of Optical Imaging 20.2.6.3 Medical Devices for Optical Imaging 20.2.7 Radionuclide Imaging 20.2.7.1 Radionuclide Imaging Benefits 20.2.7.2 Risks with Radionuclide Imaging 20.2.7.3 Scientific Radionuclide Imaging Applications 20.3 Tools for Image Processing 20.3.1 Medical Images 20.3.2 Medical Imaging and Its Properties 20.3.3 Medical Image Processing Tools 20.3.4 Medical Images Processing (MIP) 20.3.4.1 VTK 20.3.4.2 ITK 20.3.4.3 FSL 20.3.4.4 SPM 20.3.4.5 GIMIAS 20.3.4.6 NiftyReg 20.3.4.7 Elastix 20.3.4.8 ANTs 20.3.4.9 NiftySeg 20.3.4.10 ITK-Snap 20.3.4.11 MITK 20.3.4.12 NiftyRec 20.3.4.13 NiftySim 20.3.4.14 Camino 20.3.4.15 DTI-TK 20.4 Conclusion References 21: Artificial Intelligence in Bioinformatics 21.1 Introduction 21.2 Overview of Artificial Intelligence 21.2.1 Classification of Artificial Intelligence 21.2.2 Importance of Artificial Intelligence 21.2.3 Limitations of Artificial Intelligence 21.3 Application of Artificial Intelligence (AI) 21.4 Working of Artificial Intelligence 21.5 Overview of Bioinformatics 21.5.1 Challenges in Bioinformatics 21.5.2 Bioinformatics Applications 21.6 Usage of Artificial Intelligence in Bioinformatics 21.6.1 Genetic Algorithm 21.6.2 Use of Artificial Intelligence in DNA Sequencing 21.7 Conclusion and Future Direction References 22: Big Data Analysis in Bioinformatics 22.1 Introduction 22.2 Big Data and Bioinformatics 22.3 Big Data Problems and Bioinformatics 22.3.1 Gene-Gene Network Analysis 22.3.2 Microarray Data Analysis 22.3.3 Pathway Analysis 22.3.4 PPI Data Analysis 22.3.5 Evolutionary Research 22.3.6 Disease Network Analysis 22.3.7 Sequence Analysis 22.4 Big Data Analytics Techniques 22.5 Big Data Analytics and Architectures 22.5.1 MapReduce Architecture 22.5.2 Fault-Tolerant Graph Architecture 22.5.3 Streaming Graph Architecture 22.6 Big Data and Machine Learning 22.6.1 Supervised Learning 22.6.2 Unsupervised Learning 22.6.3 Reinforcement Learning 22.6.4 Deep Learning and Neural Networks 22.7 Big Data Analytics Challenges and Issues 22.7.1 Big Data Analytics and Challenges 22.7.2 Big Data Analytics and Issues 22.8 Big Data Analytics Tools and Bioinformatics 22.9 Conclusion References 23: Soft Computing in Bioinformatics 23.1 Introduction 23.2 Necessity of Soft Computing in Bioinformatics 23.3 Soft Computing Techniques and Applicability in Bioinformatics 23.3.1 Fuzzy Systems and Fuzzy Clustering 23.3.2 Neural Networks and Support Vector Machines 23.3.3 Evolutionary Computation 23.3.4 Hybrid Intelligent System 23.3.4.1 Learning Algorithm of Hybrid Intelligent Model 23.3.5 Ant Colony Optimization (ACO). 23.3.6 Particle Swarm Optimization 23.4 Case Study 23.4.1 Promoter Gene Sequence (DNA) Problem 23.4.2 Primate Splice-Junction Gene Sequence Problem 23.5 Conclusions References