Bioinformatics and Computational Biology: Technological Advancements, Applications and Opportunities

✍ Scribed by Tiratha Raj, Singh Hemraj, Saini Moacyr, Comar Junior

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 376
• Category: Library

✦ Synopsis

Bioinformatics and Computational Biology: Technological Advancements, Applications and Opportunities is an invaluable resource for general and applied researchers who analyze biological data that is generated, at an unprecedented rate, at the global level. After careful evaluation of the requirements for current trends in bioinformatics and computational biology, it is anticipated that the book will provide an insightful resource to the academic and scientific community. Through a myriad of computational resources, algorithms, and methods, it equips readers with the confidence to both analyze biological data and estimate predictions.

The book offers comprehensive coverage of the most essential and emerging topics

Cloud-based monitoring of bioinformatics multivariate data with cloud platforms
Machine learning and deep learning in bioinformatics
Quantum machine learning for biological applications
Integrating machine learning strategies with multiomics to augment prognosis in chronic diseases
Biomedical engineering
Next generation sequencing techniques and applications
Computational systems biology and molecular evolution
While other books may touch on some of the same issues and nuances of biological data analysis, they neglect to feature bioinformatics and computational biology exclusively, and as exhaustively. This book's abundance of several subtopics related to almost all of the regulatory activities of biomolecules from where real data is being generated brings an added dimension.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
Foreword
Preface
Acknowledgment
Contributors
Section A: Computer Science Techniques and Their Biological Applications
Chapter 1: Design and Analysis of Algorithms in Computational Biology
1.1 What Is an Algorithm?
1.2 Analysis of an Algorithm
1.3 The Complexity of an Algorithm
1.4 Classification of the Algorithm
1.4.1 Parallel Algorithms
1.4.2 Sequential Algorithms
1.5 Analysing Parallel Algorithms
1.5.1 Divide and Conquer Approach
1.5.2 Greedy Approach
1.5.3 Dynamic Programming
1.5.4 Backtracking Algorithm
1.5.5 Branch and Bound
1.5.6 Linear Programming
1.6 Parallel Search Algorithm
1.7 Basic Algorithms in Computational Biology
1.7.1 Classification Algorithm
1.7.2 DNA Sequence Classification
1.7.3 Clustering Algorithms
1.7.4 DNA Sequence Clustering
1.7.5 Alignment Methods
1.7.6 DNA Sequence Pattern Mining
1.7.7 Longest Common Subsequence (LCS)
1.7.8 Nussinov Algorithm
1.7.9 Heuristics
1.8 Conclusion
References
Chapter 2: Sequence-Based Algorithms in Computational Biology
2.1 Algorithm-Introduction
2.1.1 Classification of Biological Algorithm
2.2 Algorithms Related to Sequence Analysis
2.2.1 Dynamic Programming Algorithm for Pairwise Alignment
2.2.1.1 Needleman–Wunsch Algorithm
2.2.1.2 Smith–Waterman Algorithm
2.2.2 Heuristic Local Alignment
2.2.3 Sequence Analysis through Machine Learning
2.2.4 Hidden Markov Models
2.2.5 Neural Networks
2.3 Next-Generation Sequencing
2.3.1 Application
2.4 Conclusion
References
Chapter 3: Structure-Based Algorithms in Computational Biology
3.1 Basic Context: An Introduction
3.2 Algorithms for RNA Structure Prediction and Analysis
3.2.1 RNA Folding Algorithms
3.2.1.1 Nussinov Algorithm to Predict Secondary RNA Structures
3.2.1.2 Machine Learning Solves RNA Puzzles
3.2.1.3 Geometric Deep Learning of RNA Structure
3.2.1.4 GPU-Based Acceleration of an RNA Tertiary Structure Prediction Algorithm
3.2.2 Other Important Algorithms for RNA Structure Prediction using Various Methods
3.2.2.1 Prediction of Structured Motifs in the Genomic Promoter Strings using an Efficient Algorithm
3.2.2.2 Fast and Accurate Structure Probability Estimation for Simultaneous Alignment and Folding of RNAs with Markov Chains
3.2.2.3 An Efficient Simulated Annealing Algorithm for the RNA Secondary Structure Prediction with Pseudoknots
3.2.2.4 RNA Secondary Structure Prediction through Deep Neural Network Algorithm with Free Energy Perturbation or Thermodynamic Integration
3.2.2.5 Energy-Directed RNA Structure Prediction
3.2.2.6 Chemical Reaction Optimization Algorithm for RNA Secondary Structure Prediction with Pseudoknots
3.2.2.7 An Algorithm for Ranking RNA 3D Structures
3.2.2.8 Effectiveness of the Thermodynamics Algorithms for RNA Secondary Structure Prediction
3.2.3 A Novel Algorithm for RNA Secondary Structure Design
3.2.3.1 MoiRNAiFold: A Computational Tool for RNA Design
3.3 Algorithms for Protein Structure Analysis
3.3.1 Fast Quantum Hydrophobic and Hydrophilic Algorithm for Protein Structure Prediction
3.3.2 Deep Learning Inter-Residue Orientations Algorithm for Protein Structure Prediction
3.3.3 Homology Modeling Algorithms for Protein Structure Prediction
3.3.4 An Efficient Ant Colony Optimization Algorithm for Protein Structure Prediction
3.3.5 Ab Initio Protein Structure Prediction through Genetic Algorithm using Low-Resolution Model
3.4 Popular Algorithms in Drug Discovery
3.4.1 LEADD: Lamarckian Evolutionary Algorithm for De Novo Drug Design
3.4.2 Drug Discovery and Development using Graph Machine Learning Algorithm
3.5 Conclusion
References
Chapter 4: Exact, Heuristics and Other Algorithms in Computational Biology
4.1 Introduction
4.2 Alignments Methods
4.3 Dot Matrix Method
4.4 Dynamic Programming
4.5 Gap Penalties
4.6 Needleman–Wunsch Global Alignment Algorithm
4.7 Dynamic Programming for Local Alignment
4.8 Heuristic Methods
4.9 BLAST (Basic Local Alignment Search Tool)
4.10 Heuristic Approaches
4.10.1 BLAST Algorithm
4.10.2 Steps Involved in the BLAST Tool
4.10.3 BLAST Variants
4.10.4 Statistical Significance
4.10.5 BLAST Output
4.11 FASTA
4.12 Statistical Significance
4.13 Comparison of BLAST and FASTA
4.14 Conclusion
References
Chapter 5: Cloud-Based Monitoring of Bioinformatics Multivariate Data with Cloud Platforms
5.1 Introduction
5.2 Motivation to Use Cloud Computing
5.3 Components and System Architecture
5.3.1 Layer Use for User Interfaces
5.3.1.1 Workflow and Process
5.3.1.2 Cloud Integration with Multivariate Bioinformatics
5.3.1.3 SaaS Modeller using Container Orchestration
5.3.2 Middleware Services for Service Interface with Multivariate Data Analysis
5.3.2.1 Bioinformatics
5.3.3 Compute and Data Resources
5.3.3.1 Amazon EC2 Cloud Services for Container Management and Execution
5.4 Bioinformatics Applications Handling using Service-Oriented Computing Paradigm
5.4.1 Bioinformatics Applications (BioVLAB-Protein, BioVLAB-Microarray, BioVLAB-MMIA) Handling using Kubernetes Platform
5.5 Conclusion
References
Chapter 6: Machine Learning and Deep Learning in Bioinformatics
6.1 Introduction
6.2 Machine Learning
6.3 Types of Machine Learning
6.4 Machine Learning Methods
6.4.1 Bayesian Belief Networks (Bayesian Model)
6.4.2 Hidden Markov Model
6.4.3 Gaussian Mixture Model
6.4.4 Clustering Methods
6.5 Deep Learning
6.6 Neural Networks
6.7 Main Types of Neural Networks and Their Applications in Bioinformatics
6.7.1 Deep Neural Networks
6.7.2 Convolution Neural Networks
6.7.3 Recurrent Neural Network (RNN)
6.8 Deep Learning Tools and Machine Learning–Based Companies
References
Chapter 7: Quantum Machine Learning for Biological Applications
7.1 Introduction
7.2 Classical Machine Learning and Deep Learning Approach
7.2.1 Deep Learning Approaches
7.3 Quantum Computing (QC)
7.3.1 Quantum Machine Learning (QML)
7.3.2 Deep Quantum Learning (DQL)
7.4 Quantum Machine Learning Model for Biological Applications
7.4.1 Drug Discovery
7.4.2 Protein Function Prediction
7.4.3 Drug Target Interaction
7.5 Tools for QML
7.6 Challenges and Future Scope
References
Chapter 8: Integrating Machine Learning Strategies with Multiomics to Augment Prognosis of Chronic Diseases
8.1 Introduction
8.1.1 Multiomics Technology, Artificial Intelligence and Machine Learning Approaches
8.2 Multiomics and Integrated Machine Learning in Respiratory Disorders
8.2.1 COVID-19
8.2.2 Tuberculosis
8.2.3 Chronic Obstructive Pulmonary Disease
8.3 Multiomics and Integrated Machine Learning Approaches in Cardiovascular Disorders
8.3.1 Heart Failure
8.3.2 Atrial Fibrillation and Valvular Heart Disease
8.4 Challenges
8.5 Advantages
8.6 Future Directions
8.7 Conclusion
References
Chapter 9: Deep Learning Piloted Drug Design: An Advantageous Alliance
9.1 Introduction
9.2 Application of DL Methodologies at Various Stages of Drug Design
9.2.1 Target Identification and Validation
9.2.2 Lead Identification and Optimization
9.2.3 Virtual Screening
9.2.4 Predictive Toxicology
9.2.5 Quantitative Structure-Activity Relationship (QSAR)
9.3 Benefits of DL in Drug Designing
References
Chapter 10: Drug Discovery by Deep Learning and Virtual Screening: Review and Case Study
10.1 Introduction
10.2 Machine Learning–Based Algorithms
10.2.1 Artificial Neural Networks (ANNs)
10.2.2 Support Vector Machine (SVM)-Based Techniques
10.2.3 Kohonen’s Self-Organizing Maps (SOMs)
10.2.4 Ensemble Methods
10.2.5 Deep Learning
10.3 A Case Study: Utilization of CNN to Classify Compounds
10.4 Final Considerations
References
Section B: Algorithms for Sequence and Structure Analysis
Chapter 11: Gene Regulation, DNA and RNA Structure and Sequencing
11.1 The Introduction and Principles of Gene Regulation
11.1.1 Essentials of Gene Regulation at the Transcriptional Level in Eukaryotes
11.1.2 The Basal Transcriptional Machinery in Eukaryotes
11.1.3 Post-Transcriptional Gene Regulation
11.1.4 Regulatory Noncoding RNA
11.1.5 Introduction of Bioinformatic Analysis and Algorithms of Gene Regulation
11.2 Deoxyribonucleic Acid (DNA) Structure, Introduction, and Fundamentals
11.2.1 Phylogenetic, Computational, and Evolutionary Aspects of DNA Structure
11.2.2 Algorithms for DNA Structure Prediction and Analysis
11.2.3 Pathophysiology of DNA Structure and Clinical Significance
11.2.4 DNA Sequencing
11.3 Ribonucleic acid (RNA) Structure, Introduction, Architecture, and Fundamentals
11.3.1 Regulation and Post-transcriptional Modifications in RNAs
11.3.2 Phylogenetic, Computational, and Evolutionary Aspects of RNA Structure
11.3.3 Algorithms for Analysis of RNA Structure Prediction and Analysis
11.3.4 Pathophysiology of RNA Structure and Clinical Significance
11.3.5 RNA Sequencing
11.4 Latest in Sequencing and Bioinformatics Tools/Technologies to Study Omics Data
References
Chapter 12: Computational Prediction of RNA Binding Proteins: Features and Models
12.1 Introduction
12.2 Computational Methods for Predicting RBPs
12.2.1 Dataset for RBP Recognition
12.2.2 Feature Representation
12.2.2.1 Sequence-Based Features
12.2.2.1.1 Amino Acid Composition (AAC)
12.2.2.1.2 Tri-peptide Composition (TPC)
12.2.2.1.3 Evolutionary Features
12.2.2.2 Structure-Based Features
12.2.2.2.1 The Secondary Structure (SS)
12.2.2.2.2 Accessible Solvent Area (ASA)
12.2.2.3 Physicochemical Features
12.2.2.3.1 Hydrophobicity
12.2.2.3.2 Electrostatic Patches
12.2.3 Computational Models Available for Prediction of RBPs
12.3 Conclusions and Future Perspectives
References
Chapter 13: Fundamental and Best Practices for Protein Annotations
13.1 Introduction
13.2 Standard Tools for the Analysis of Proteins
13.3 Biological and Functional Annotations
Acknowledgments
References
Chapter 14: Microarray Data Analysis: Methods and Applications
14.1 Introduction
14.2 Microarray Technology
14.3 Methods for Microarray Data Analysis
14.4 Microarray Data Analysis Tools and Software
14.5 Applications of Microarray Analysis
14.6 Conclusion
References
Chapter 15: Tools and Techniques in Structural Bioinformatics
15.1 Relationship Websites
15.2 Databases
15.3 Tertiary Structure Prediction
15.4 Docking, Virtual Screening and Virtual High-Throughput Screening
15.5 Molecular Dynamics Simulations
References
Chapter 16: A Streamline to New Face of Drug Discovery by Protein–Protein and Protein–Nucleic Acid Interactions
16.1 Introduction to Drug Discovery
16.2 Structure-Based Drug Discovery
16.2.1 Protein Structures (PDB and Modeling)
16.2.2 Binding Site and Allosteric Site
16.2.3 Interaction Analysis
16.3 Protein-Protein Docking a New Attempt in Drug Development
16.3.1 Protein Recognition
16.3.2 List of Tools Used
16.3.2.1 Online Tools
16.3.2.2 Commercial Software and Offline User Tools
16.4 Protein-Peptide Docking and Its Tools
16.4.1 Types of Protein-Peptide Docking
16.4.1.1 Template-based Docking
16.4.1.2 Local Docking
16.4.1.3 Global Docking
16.4.2 Tools for Protein-Peptide Docking
16.5 Protein Nucleic Acid Complexes: A New Trend to Drug Discovery
16.5.1 Types of Protein–Nucleic Acid Interactions
16.5.1.1 Protein-DNA Interaction
16.5.1.2 Protein-RNA Interaction
16.5.2 Tools Used
16.5.2.1 NP Dock
16.5.2.2 HDock
16.5.2.3 HNADock
16.6 Conclusion
Acknowledgments
Author Contribution Statement
Data Availability
Compliance with Ethical Standards
Conflict of Interest
Source of Funding
References
Section C: Advanced Computational Biology Techniques and Applications
Chapter 17: From Proteins to Networks: Assembly, Interpretation, and Advances
17.1 Introduction
17.2 The Information Flow in the Cell
17.3 Protein-Protein Interaction Networks (PPINs)
17.4 Why Do We Need Computational PPINs?
17.5 Reconstruction of a PPI Network
17.5.1 Transcriptome Data Integration in Protein Interaction Network
17.6 Quality Assessment of Computer Generated PPINs
17.6.1 Comparisons with Random Networks
17.6.2 Comparison with Real Biological Networks
17.6.3 Functional Similarity-based Assessment
17.7 Network Analysis
17.7.1 Hubs
17.7.2 Bottlenecks
17.7.3 Shortest Path
17.7.4 Centrality
17.7.5 Controllability
17.7.6 Network Dynamics
17.8 Functional Analysis
17.8.1 Modularity in Biological Networks
17.8.2 Clustering of Biological Networks
17.8.3 Network-based Pathway Analysis
17.9 Recent Advances in PPI Network
17.9.1 Recent Advances from Single Cell Data
17.9.2 Recent Advances in Host–Pathogen PPIN
17.10 Outlook and Future Directions
References
Chapter 18: Higher-Order Organization in Biological Systems: An Introduction
18.1 Introduction
18.2 Network Theoretic Concepts
18.2.1 Graph Definitions
18.2.1.1 Graph
18.2.1.2 Graph Isomorphism and Orbits
18.2.1.3 Sub-graphs
18.2.2 Graph-Based Measures to Capture Higher-Order Interactions
18.2.2.1 Network Motifs
18.2.2.2 Graphlets
18.2.2.3 Cliques
18.2.3 Network Representations That Explicitly Represent Higher-Order Interactions
18.2.3.1 Hypergraphs
18.2.3.2 Simplicial Complexes
18.3 Examples of Higher-Order Organization in Biological Networks
18.3.1 Protein Interaction Networks (PINs)
18.3.2 Brain Networks
18.3.3 Ecological Networks
18.3.4 Miscellaneous
18.4 Conclusion and Future Prospects
References
Chapter 19: Advancement and Applications of Biomedical Engineering in Human Health Care
19.1 Introduction
19.2 Bioinformatics and Biomedical Engineering
19.3 Biomedical Engineering in Human Health Care
19.3.1 Patient Monitoring System
19.3.2 Telemedicine
19.3.3 3D Bioprinting
19.4 Nanobiotechnology
19.4.1 Bioimaging
19.4.2 Drug Delivery
19.5 Biomedical Robotics and Computer-Assisted Surgery
19.6 Synthetic Biology
19.7 Summary
Conflict of Interest
Acknowledgment
Author Contribution
Funding
References
Chapter 20: Clinical Trials in the Realm of Health Informatics
20.1 Introduction
20.2 CT Process
20.3 Automation Methods in CT
20.3.1 Software-as-a-Service
20.3.2 Blockchain
20.3.3 Internet of Things
20.3.4 Artificial Intelligence-Based Data Analytics
20.3.5 Robotic Process Automation (RPA) in CTs
20.4 Architecture
20.5 Progress So Far
20.6 Conclusions
References
Chapter 21: Application of Genomics in Novel Microbial Species Identification
21.1 Introduction
21.2 Standard Protocols Followed for Prokaryotic Nomenclature
21.3 16S rRNA Gene Sequence Analysis
21.4 Multilocus Sequence Typing (MLST)
21.5 Next-Generation Sequencing (NGS) Approach for Microbial Taxonomy
21.6 Overall Genome Relatedness Indices (OGRI)
21.7 Average Nucleotide Identity (ANI)
21.8 Different Algorithms Used for Average Nucleotide Identity (ANI) Estimation
21.8.1 Original ANI
21.8.2 OrthoANI
21.8.3 OrthoANIu
21.8.4 ANIm
21.8.5 Average Amino Acid Identity (AAI)
21.8.6 In Silico DNA-DNA Hybridization (insDDH)
21.8.7 Maximal Unique Matches Index (MUMi)
21.9 Web-based Tools for Taxonomic Analysis of Whole Genome Sequences
21.10 Conclusion
References
Chapter 22: Next-Generation Sequencing Data Analysis
22.1 Introduction – Genomics
22.2 Analysis of Whole Genome and Exome Sequencing Data
22.3 Selective Sweep
22.4 Approaches for Genome Wide Scan to Identify Signature Genes
22.5 Detection
22.6 Transcriptomics
22.7 Quality Assessment and Pre-processing of Raw Reads
22.8 Read Alignment
22.9 Transcriptome Reconstruction
22.10 Expression Quantification
22.11 RSEM
22.12 Cufflinks
22.13 Differential Expression Analysis
22.14 Annotation and Pathway Enrichment Analysis
22.15 Metagenomics
22.16 Framework of Metagenomics
22.17 Applications
22.18 Conclusion
References
Chapter 23: Computational Molecular Evolution: A Detailed View of Phylogenetic Methods
23.1 Introduction
23.2 Molecular Phylogenetics
23.3 Why to Use Molecular Data?
23.4 Models of Molecular Evolution
23.4.1 Jukes and Cantor’s One-Parameter Model
23.4.2 Kimura’s Two-Parameter Model
23.5 Opportunities and Obstacles of Molecular Phylogenetics
23.6 Molecular Phylogenetic Analysis
23.7 Applications of Phylogenetics
23.7.1 Phylogenetic Tree
23.7.2 Re-construction of Phylogenetic Tree
23.7.3 Steps to Build a Tree
23.7.4 Types of Phylogenetic Tree
23.7.5 Parts of Phylogenetic Tree
23.8 Strategies of Tree Reconstruction
23.9 Distance Matrix Method
23.10 Unweighted Pair Group Method with Arithmetic Mean (UPGMA)
23.11 Neighbor-Joining Method
23.12 Benefits and Drawbacks of the Neighbor-Joining Approach
23.13 Maximum Parsimony Methods
23.14 Maximum Likelihood
23.15 Bayesian Inference of Phylogeny Background and Bases
23.16 Recent Developments and Applications in Various Life Domains
23.17 Conclusion
References
Chapter 24: Exploring Multifaceted Applications of Bioinformatics: An Overview
24.1 Introduction
24.2 Role of Bioinformatics in Biotechnological Research
24.2.1 Comparative Genomics
24.2.2 Microbial Genomics
24.2.3 Proteomics Veterinary Sciences
24.3 Role of Bioinformatics in Agricultural Research
24.3.1 Crop Improvement
24.3.2 Nutritional Quality of Improvement
24.3.3 Plant Breeding
24.3.4 Plant Disease Management
24.4 Role of Bioinformatics in Industrial Research
24.4.1 Genome Sequencing and Annotation
24.4.2 Enhancement of Product Quality
24.5 Role of Bioinformatics in Biopharmaceutical Research
24.5.1 Drug Discovery
24.5.1.1 Computer-Aided Drug Designing
24.5.1.1.1 Target Identification
24.5.1.1.2 Hit Identification using Virtual Screening and Molecular Docking
24.5.1.1.3 Lead Optimization
24.5.1.1.4 Optimization of ADMET Properties
24.6 Conclusion
References
Chapter 25: Industrial and Biopharmaceutical Research in Bioinformatics
25.1 Bioinformatics in Industrial Research
25.2 Industrial Biocatalysis
25.3 Microbial Biodegradation
25.4 Waste Management
25.5 Microbial Engineering
25.6 Bioremediation
25.7 Cosmetics and Probiotics
25.8 Synthetic Biology for Industrial Strain Engineering
25.9 Bioinformatics in Biopharmaceutical Research
25.10 Pharmacogenomics in Drug Discovery
25.11 Next-Generation Biopharmaceuticals
25.12 Translational Drug Discovery
25.13 Immunoinformatics
25.14 Artificial intelligence (AI) and Machine learning (ML) in Precision Medicine
25.15 Conclusion
Bibliography
Chapter 26: Biotechnological, Agriculture, Industrial, and Biopharmaceutical Research in Bioinformatics
26.1 Introduction
26.2 Biotechnological Research in Bioinformatics
26.2.1 Next-Generation Sequencing
26.3 Bioinformatics in Agriculture
26.3.1 Crop Genome Mapping
26.3.2 Genetic Editing-Based CRISPR-Cas9
26.4 Bioinformatics in Industrial Research
26.5 Bioinformatics in Biopharmaceuticals
26.5.1 Vaccine Designing
26.5.2 Novel Drug Design
26.5.3 Structure-Based Drug Discovery
26.6 Conclusion
References
Index