Phylogenomics : foundations, methods, and pathogen analysis /
Offers a deep overview of phylogenomics as a field, compelling recent developments, and detailed methods and approaches for conducting new research. Early chapters introduce phylogenomic taxonomies of organisms and pathogens, phylogenomic networks, phylogenomics of virus virulence, and ancient DNA a...
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| Autres auteurs: | , |
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| Format: | eBook |
| Langue: | anglais |
| Publié: |
London :
Academic Press,
2024.
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| Accès en ligne: | ScienceDirect |
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Table des matières:
- Front Cover
- Phylogenomics
- Copyright Page
- Contents
- List of contributors
- Preface
- I. General topics and foundations
- 1 Phylogenomic analysis and the origin and early evolution of viruses
- 1.1 Introduction
- 1.2 Retrodiction
- 1.3 The analytical basis of the phylogenetic framework
- 1.4 Rooting trees
- 1.5 Phylogenomic analysis
- 1.6 Deep evolutionary explorations with alignment-free methods
- 1.7 Untangling the origin and evolution of viruses with structural phylogenomics
- 1.8 Conclusions
- References
- 2 Application of next-generation sequencing for genetic and phenotypic studies of bacteria
- 2.1 Introduction
- 2.2 Whole genome sequence data
- 2.2.1 Read lengths and base accuracy
- 2.2.2 Data quality control
- 2.3 Reference genomes
- 2.4 Pangenome, core genome, and accessory genome
- 2.5 Principles of genotypic classification
- 2.6 Species classification and identification using whole genome sequencing data
- 2.7 Genotyping based on whole genome sequencing data
- 2.7.1 Single nucleotide variant-based genotyping
- 2.7.1.1 Criteria for genotypic classification of bacteria based on single nucleotide variant phylogeny
- 2.7.1.2 Genotyping barcodes
- 2.7.2 Genotyping by structural variants
- 2.7.2.1 Deletions
- 2.7.2.2 Insertions
- 2.7.2.3 Copy number variations
- 2.7.3 Congruence between single nucleotide variant-based genotyping and other classification methods
- 2.7.3.1 Other genotyping methods
- 2.7.3.2 Serotyping
- 2.7.4 Correlation of genotypes and phenotypes
- 2.7.4.1 Demographic information
- 2.7.4.2 Drug resistance
- 2.7.4.3 Disease severity and contagiousness
- 2.8 Genotyping and control of infectious diseases
- 2.9 Genotyping and human genetics of infectious diseases
- 2.10 Conclusion
- References
- 3 Genomic insights into deciphering bacterial outbreaks
- 3.1 Introduction.
- 3.2 Genomes and variation
- 3.3 Sequencing whole genomes
- 3.4 Outbreak delimitation
- 3.5 Forensic analysis of outbreaks and transmissions
- 3.6 Conclusion
- References
- 4 Drug resistance in bacteria, molecular mechanisms, and evolution
- 4.1 Introduction
- 4.2 What are antibiotics
- 4.2.1 Historical perspective
- 4.3 Mechanisms of bacterial resistance to antibiotics
- 4.3.1 Countering presence/entry of antibiotics
- 4.3.2 Antibiotics destroying mechanisms in vivo
- 4.3.3 Antibiotic target alterations
- 4.3.4 Heteroresistance
- 4.4 Evolution of antibiotic resistance
- 4.4.1 Chromosomal mutations
- 4.4.2 Acquisition of genetic material
- 4.4.3 Ancient origins
- 4.4.4 Cost of resistance to bacteria: bacterial fitness
- 4.5 Classes of antibiotics, mechanisms of action and resistance
- 4.5.1 Disruptors of DNA synthesis
- 4.5.1.1 Sulfonamides
- 4.5.1.2 Quinolones
- 4.5.1.3 Nitroimidazoles
- 4.6 Disruptors of cell envelope
- 4.6.1 Beta-lactams
- 4.6.2 Glycopeptides and lipoglycopeptides
- 4.6.3 Lipopeptides (Daptomycin)
- 4.6.4 Polypeptides (Bacitracin)
- 4.7 Other antibiotics
- 4.7.1 Aminoglycosides (gentamicin, tobramycin, amikacin, netilmicin, plazomicin, kanamycin, streptomycin, and paromomycin)
- 4.7.2 Polymyxins
- 4.7.3 Bacteriostatic protein synthesis inhibitors
- 4.7.3.1 Tetracyclines, tigecycline, and eravacycline
- 4.7.3.2 Macrolides (erythromycin, clarithromycin, azithromycin, and fidaxomicin)
- 4.7.4 Lincosamides
- 4.7.5 Oxazolidinones
- 4.7.6 Pleuromutilins
- 4.7.6.1 Streptogramins (quinupristin: dalfopristin, 30:70)
- 4.7.7 Chloramphenicol
- 4.7.8 Mupirocin
- 4.8 Origin and evolution of bacterial resistance
- 4.8.1 Integrons
- 4.8.2 Phages
- 4.8.3 Resistance evolution mechanisms
- 4.8.3.1 Efflux pumps
- 4.8.3.2 Acquired efflux mechanisms
- 4.8.4 Beta-lactams
- 4.8.5 Ceftazidime.
- 4.8.6 Quinolones
- 4.8.7 Colistin
- 4.8.8 New compounds
- 4.9 Newer approaches to deal with antimicrobial resistance
- 4.9.1 Integrons with dysfunctional integrase (antievolution drugs)
- 4.9.2 COM blockers
- 4.9.3 Potentiator genes/evolutionary catalysts
- 4.9.4 Evolvability factors
- 4.10 Conclusions
- References
- 5 Virulence evolution of bacterial species
- 5.1 Introduction
- 5.2 What is virulence?
- 5.3 Models for the evolution of virulence
- 5.3.1 Avirulence hypothesis or law of declining virulence
- 5.3.2 Trade-off hypothesis
- 5.3.2.1 "Short-sighted" evolution of virulence
- 5.3.3 Coincidental evolution of virulence
- 5.4 Molecular infection biology
- 5.5 Virulence factors and mechanisms in Gram-negative pathogens
- 5.5.1 Enterobacterales
- 5.5.2 Vibrio
- 5.5.3 Pseudomonas
- 5.5.4 Campylobacter
- 5.5.5 Helicobacter
- 5.5.6 Neisseria
- 5.5.7 Haemophilus
- 5.6 Virulence factors and mechanisms in Gram-positive bacteria
- 5.6.1 Staphylococcus
- 5.6.2 Streptococcus
- 5.6.3 Enterococcus
- 5.6.4 Bacillus
- 5.6.5 Listeria
- 5.6.6 Clostridium
- 5.7 Acid-fast bacteria
- 5.7.1 Mycobacterium
- 5.8 Final remarks
- References
- II. Methods in the phylogenomics
- 6 Modeling evolutionary changes of k-mer patterns of bacterial genomes
- 6.1 Introduction
- 6.2 Estimation of evolutionary distances by comparison of k-mer patterns
- 6.3 Driving forces on the k-mer pattern evolution
- 6.4 Using graph models in investigation of k-mer pattern evolution
- 6.5 Conclusion
- References
- 7 Clock rates and Bayesian evaluation of temporal signal
- 7.1 Introduction
- 7.2 TMRCAs and mutation rate
- 7.3 Tip-dating and tip randomization
- 7.4 Population changes through time
- 7.5 Case study: Is there a molecular clock in Mycobacterium tuberculosis?
- 7.6 Models limits and sensitivity
- 7.7 Fluctuating mutation rates.
- 7.8 The rise of fine-tuned phyloepidemiology models
- 7.9 Conclusion and perspectives
- References
- 8 Microbial evolutionary reconstruction in the presence of mosaic sequences
- 8.1 Introduction
- 8.2 Biological processes giving rise to viral and bacterial mosaic sequences
- 8.2.1 Recombination in viruses
- 8.2.1.1 Recombination in RNA viruses
- 8.2.1.2 Recombination in DNA viruses
- 8.2.1.3 Reassortment in viruses with segmented genomes
- 8.2.2 Genetic recombination and horizontal gene transfer in prokaryotes
- 8.2.3 Long-distant horizontal gene transfer in viruses
- 8.2.4 Incomplete lineage sorting
- 8.3 Mosaic sequence and structure detection methods
- 8.3.1 Methods for detecting the presence of mosaic sequences in the dataset
- 8.3.2 Methods for detecting mosaic sequences and transferred/exchanged regions
- 8.3.2.1 Explicit phylogenetic methods
- 8.3.2.2 Implicit phylogenetic methods
- 8.3.2.3 Parametric methods
- 8.3.3 An illustrative example-detecting recombination regions within simian foamy viruses' env gene
- 8.4 Potential impacts of mixed evolutionary signals on traditional phylogenetic reconstruction
- 8.4.1 Potential impacts of mixed evolutionary signals on traditional phylogenetic reconstruction
- 8.4.2 An illustrative example-the phylogenetic reconstruction of simian foamy viruses' env gene with and without considerat...
- 8.5 Tree-based approaches to dealing with mosaic sequences and mixed evolutionary signals
- 8.5.1 Removing mosaic sequences/regions from the dataset
- 8.5.2 Supertree approach
- 8.5.3 Considering trees of genomic regions of different evolutionary pasts together
- 8.6 Network-based approaches to reconstructing an entangled evolutionary history
- 8.6.1 Phylogenetic network-how does it differ from a phylogenetic tree?
- 8.6.2 Various kinds of phylogenetic networks.
- 8.6.3 Implicit phylogenetic networks
- 8.6.4 Explicit phylogenetic networks
- 8.6.5 An illustrative example-phylogenetic networks of simian foamy viruses' env gene
- 8.6.5.1 An implicit phylogenetic network of simian foamy viruses' env gene
- 8.6.5.2 An explicit phylogenetic network of simian foamy viruses' env gene
- 8.7 Final remarks
- References
- 9 Tools for short variant calling and the way to deal with big datasets
- 9.1 Introduction
- 9.2 Types of whole genome sequencing data
- 9.3 Pretreatment of data
- 9.3.1 Trimming and deduplication
- 9.3.2 Decontamination
- 9.3.2.1 Evaluating the amplitude of contamination
- 9.3.2.2 Alignment-based exclusion of reads from distant species
- 9.3.2.3 K-mer-based exclusion of reads from distant species
- 9.3.2.4 Taxonomic classifiers to detect and remove contaminants
- 9.3.2.5 Potential impact of contamination on variant calling
- 9.4 Calling of short variants
- 9.4.1 Alignment-based variant calling, single nucleotide variants, and short indels as compared to a reference
- 9.4.1.1 Procedure
- 9.4.1.2 Importance of the reference
- 9.4.1.3 Filtering parameters/thresholds
- 9.4.1.4 Interactions between aligners and variant callers
- 9.4.1.5 Alignment-based variant calling performance
- 9.4.1.6 Machine learning for variant discovery improvement
- 9.4.2 k-mer-based methods for variant calling
- 9.5 Postprocessing of variants
- 9.6 Large datasets and computational solutions to deal with them
- 9.6.1 Volume
- 9.6.2 Velocity
- 9.7 All-in-one pipelines
- 9.7.1 Diversity of all-in-one pipelines proposed for Mycobacterium tuberculosis
- 9.7.2 General assessment of all-in-one pipelines and perspectives for future tools
- 9.8 Conclusion
- Funding
- References
- III. Phylogenomics of specific pathogens
- 10 Phylogenomics of Yersinia pestis
- 10.1 Introduction.