Viruses are the most numerous and deadliest biological entities on the planet, infecting all types of living organisms―from bacteria to human beings. The constantly expanding repertoire of experimental approaches available to study viruses includes both low-throughput techniques, such as imaging and 3D structure determination, and modern OMICS technologies, such as genome sequencing, ribosomal profiling, and RNA structure probing. Bioinformatics of viruses faces significant challenges due to their seemingly unlimited diversity, unusual lifestyle, great variety of replication strategies, compact genome organization, and rapid rate of evolution. At the same time, it also has the potential to deliver decisive clues for developing vaccines and medications against dangerous viral outbreaks, such as the recent coronavirus pandemics. Virus Bioinformatics reviews state-of-the-art bioinformatics algorithms and recent advances in data analysis in virology.
FEATURES
- Contributions from leading international experts in the field
- Discusses open questions and urgent needs
- Covers a broad spectrum of topics, including evolution, structure, and function of viruses, including coronaviruses
The book will be of great interest to computational biologists wishing to venture into the rapidly advancing field of virus bioinformatics as well as to virologists interested in acquiring basic bioinformatics skills to support their wet lab work.
Author(s): Dmitrij Frishman, Manja Marz
Series: Computational Biology Series
Publisher: CRC Press
Year: 2021
Language: English
Pages: 296
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
CHAPTER 1 Comparative Genomics of Viruses
1.1 Genomics of Viruses
1.1.1 Genome Types, Sizes, and Nomenclature
1.1.2 Genome Sequences from Cultures
1.1.3 Genomes from Environmental Samples
1.1.4 Proviruses
1.1.5 Annotation of Virus Genomes
1.1.6 Database Resources for Virus Genome Sequences
1.2 Comparison of Virus Genome Sequences
1.3 Protein Families and Orthologous Groups of Viruses
1.4 Evolution of Protein Families within Virus and Host Genomes
1.5 Outlook
References
CHAPTER 2 Current Techniques and Approaches for Metagenomic Exploration of Phage Diversity
2.1 Introduction
2.2 Phage Metagenomics: A Brief History
2.3 Recovering Phage Genomes from Metagenomes
2.4 Identification and Quality Control of Phage Contigs in Metagenome Assembly
2.5 Structural Annotation of Metagenome-Assembled Phage Genomes
2.6 Bringing Meaningful Eco-evolutionary Context to Metagenome-Assembled Phage Genomes
2.7 Conclusion
References
CHAPTER 3 Direct RNA Sequencing for Complete Viral Genomes
3.1 Advantages and Disadvantages for Viruses
3.2 Virus Assembly of the Human Coronavirus 229E
3.3 Long Reads Enable Discovery of Long-Range Interactions and Genome-Wide Compensatory Mutations
3.4 Sequencing Full RNA Viral Transcripts
3.5 Modifications
3.6 Tracking Virus Mutations during Outbreaks
Acknowledgments
References
CHAPTER 4 Computational Methods for Viral Quasispecies Assembly
4.1 Introduction
4.2 Challenges of Global Haplotype Reconstruction
4.3 Overview of Methodological Approaches for Global Haplotype Reconstruction
4.4 Conclusions
References
CHAPTER 5 Functional RNA Structures in the 3' UTR of Mosquito-Borne Flaviviruses
5.1 Introduction
5.2 Flavivirus 3' UTR Background
5.3 Materials and Methods
5.4 Results
5.4.1 Japanese Encephalitis Virus Group
5.4.2 Ntaya Virus Group
5.4.3 Aroa Virus Group
5.4.4 Kokobera Virus Group
5.4.5 Dengue Virus Group
5.4.6 Spondweni/ Kedougou Virus Group
5.4.7 Yellow Fever Virus Group
5.4.8 Structural Diversity of Conserved Elements
5.5 Conclusion
References
CHAPTER 6 Structural Bioinformatics of Influenza Virus RNA Genomes
6.1 Introduction
6.2 Detection of Conserved Structures in Influenza Virus RNA by Comparative Analysis
6.3 Identification of Influenza Virus RNA Structures Using Structure Probing
6.4 Networks of Intersegmental Interactions
6.5 Concluding Remarks
References
CHAPTER 7 Structural Genomics and Interactomics of SARS-COV2: Decoding Basic Building Blocks of the Coronavirus
7.1 Understanding the Molecular Mechanisms of COVID-19: A Current Focus of Scientific Community
7.2 Genomic and Structural Organization of the Novel Coronavirus
7.3 Structural Characterization of the Individual Viral Proteins
7.4 Structural Characterization of Intra-Viral and Viral-Host Protein Complexes
7.5 Molecular Interactions between Viral Proteins and Small Ligands
7.6 Virus-Host Interactions: A Systems View
7.7 Next Steps
References
CHAPTER 8 Computational Tools for Discovery of CD8 T cell Epitopes and CTL Immune Escape in Viruses Causing Persistent Infections
8.1 Impact of Viral Mutations in Amino Acid Sequence of Viral Proteins on Epitope Recognition during Chronic Infection
8.2 HDV as a Model for Detection of Epitopes and Corresponding Immune Escape Mutation in Chronic Viral Infection
8.3 HDV Molecular Biology and Replication
8.4 HDV Genome Variability of a Sequence
8.5 HDV Immunology
8.6 Methods for the Prediction of CTL Epitopes and the Detection of IEMs
8.7 Better Ways of Finding HLA-Associated Mutations (HAMs)
8.8 Conclusion
References
CHAPTER 9 Virus-Host Transcriptomics
9.1 Introduction
9.2 Parallel Read Alignment to Host and Virus
9.3 Incomplete Annotation of Viral Transcriptomes
9.4 Normalization and Differential Gene Expression Analysis
9.5 Be Careful of Your Interpretation
9.6 Conclusion
References
CHAPTER 10 Sequence Classification with Machine Learning at the Example of Viral Hos Prediction
10.1 Machine Learning Applications in Virology
10.2 An Introduction to Neuronal Networks—and When to Use Them
10.3 Machine Learning as a Powerful Method to Classify Viral Sequences
10.3.1 Final Host Prediction From Subsequence Predictions
10.4 The Host of a 400 nt Fragment of Influenza A Virus Can Be Predicted Very Accurately
10.4.1 Varying the Details Yield Similar Predictions
10.5 Final Remarks
References
CHAPTER 11 Master Regulators of Host Response to SARS- CoV-2 as Promising Targets for Drug Repurposing
11.1 Introduction
11.2 Results
11.2.1 Functional Classification of Genes
11.2.2 Analysis of Enriched Transcription Factor Binding Sites and Composite Modules
11.2.3 Finding Master Regulators in Networks
11.2.4 Finding Prospective Drug Targets
11.2.5 Identification of Potential Drugs
11.2.5.1 Drugs Approved in Clinical Trials
11.2.5.2 Repurposing Drugs
11.3 Discussion
11.4 Methods
11.4.1 Databases Used in the Study
11.4.2 Methods for the Analysis of Enriched Transcription Factor Binding Sites and Composite Modules
11.4.3 Methods for Finding Master Regulators in Networks
11.4.4 Methods for Analysis of Pharmaceutical Compounds
11.4.4.1 Method for Analysis of Known Pharmaceutical Compounds
11.4.4.2 Method for Prediction of Pharmaceutical Compounds
References
CHAPTER 12 The Potential of Computational Genomics in the Design of Oncolytic Viruses
12.1 Introduction
12.2 Mathematical Modeling of OV
12.3 Computational Modeling of Heterologous Gene Expression and Live Attenuated Vaccines
12.4 The Potential of Bioinformatics and Genomics in the Development of OV
12.5 Conclusion
References
CHAPTER 13 Sharing Knowledge in Virology
13.1 The Virus Exception
13.2 ViralZone at the Service of Knowledge Sharing
13.3 The Growing Landscape of Virus Databases
13.4 The Predictive Power of Knowledge
13.5 Conclusion
References
INDEX
