Transcriptome Profiling: Progress and Prospects

Front Cover
Transcriptome Profiling
Copyright Page
Contents
List of contributors
Preface
1 Transcriptomic analysis of genes: expression and regulation
	1.1 Techniques for transcription analysis and RNA-seq profiling
	1.2 Sequencing platforms and gene analysis workflow for genome and transcriptome assembly
		1.2.1 Fungal genomes and transcriptomes
	1.3 Expression and differential expression analysis: methods and programs
		1.3.1 Counting methods
		1.3.2 Data normalization and statistical procedures
		1.3.3 Comparison of different expression profiles
		1.3.4 Validation
	1.4 Data integration techniques for coexpression network construction
		1.4.1 Coexpression networks
	1.5 Gene regulation studies on bacteria and fungi
		1.5.1 Expression studies on fungi/host interactions
		1.5.2 Expression analysis to study fungal responses to compounds/detoxification
	1.6 Application of transcriptomics to the study of small RNAs, transcription factors, heat shock factors, kinases (MAPK), P...
		1.6.1 Small RNAs
		1.6.2 Transcription factors
		1.6.3 Mitogen-activated protein kinase cascade
		1.6.4 Fungal metabolites
	1.7 Transcriptomic studies of genetic engineering approaches
	1.8 Application of transcriptomics in the context of diseases and clinical studies
		1.8.1 RNA-seq for disease research
		1.8.2 Understanding microbial infections using transcriptional analysis
	References
2 Transcriptomics and genetic engineering
	2.1 Introduction
	2.2 History
	2.3 Transcriptomics
	2.4 Gene ontology
	2.5 Genetic engineering approaches to target the transcriptome
	2.6 Model organisms for transcriptome research
	2.7 Challenges and conclusion
	Authors contributions
	Financial support
	Competing interests
	References
	Further reading
3 Single-cell transcriptomics
	3.1 Introduction
	3.2 Measurement techniques in single-cell transcriptomics
		3.2.1 Spatial single-cell methods
		3.2.2 Transcriptomic analysis with 10X genomics platform
	3.3 Noise in single-cell sequencing
		3.3.1 Problem with zeros
		3.3.2 Experimental protocols used to decrease the noise level
	3.4 Preprocessing of 10X scRNAseq data
		3.4.1 From raw sequences to gene−cell matrix
		3.4.2 Quality control
		3.4.3 Data normalization
		3.4.4 Batch effect removal
	3.5 Analysis of 10X scRNAseq data
		3.5.1 Cross-platform/species data integration
		3.5.2 Searching for differentially expressed genes
		3.5.3 Cell clustering
		3.5.4 Cellular trajectory inference
	References
4 Time course gene expression experiments
	4.1 Introduction
	4.2 Designing time course experiments
	4.3 A holistic method to analyze time course gene expression experiments
		4.3.1 Standardized expression profiles for interpretation of expression profiles
		4.3.2 Ternary models simplify the expression space
		4.3.3 Standardized expression profile groups can be tested to investigate relevant differences
		4.3.4 Standardized expression profiles allow the discovery of enriched gene ontology aspects and metabolic pathways
	4.4 Conclusions and perspectives
	4.5 Appendix: standardized expression profile estimation
		4.5.1 Statistical tests
		4.5.2 Ternary model and standardized expression profile estimation
	References
5 Measurement and meaning in gene expression evolution
	5.1 Introduction
		5.1.1 Extended concepts in evolution
	5.2 What is gene expression?
		5.2.1 The conditional transfer of sequence information
		5.2.2 Phenotypic outcome in regulatory development
	5.3 Gene expression evolution
		5.3.1 Bias in the sorting of difference-making loci
		5.3.2 Bias in the recurrence of phenotypes
	5.4 Measuring gene expression
		5.4.1 Relative change in mRNA abundance
		5.4.2 Coexpression networks
		5.4.3 Technological developments
	5.5 Measuring gene expression evolution
		5.5.1 Divergent phenotypic variance
		5.5.2 Divergent interaction networks
		5.5.3 Future prospects
	References
6 G-quadruplexes as key motifs in transcriptomics
	Abbreviations
	6.1 Introduction
	6.2 G-quadruplexes
	6.3 Approaches to identify G4s
	6.4 Functions of G4s
		6.4.1 Telomere maintenance
		6.4.2 Transcription
		6.4.3 mRNA maturation
		6.4.4 mRNA localization
		6.4.5 Translation
		6.4.6 Biology of noncoding RNAs
		6.4.7 Epigenetics
	6.5 Genome instability associated to G4s
	6.6 G4-binding proteins
	6.7 G4s’ involvement in disease
	6.8 G4 Ligands
	6.9 Future perspectives
	References
7 Spatial transcriptomics
	7.1 An introduction to spatial transcriptomics
	7.2 Origin of spatial transcriptomics
	7.3 Implementation of a spatial transcriptomics study: tools and techniques
		7.3.1 Broad overview of spatial transcriptomics techniques
		7.3.2 Visium
		7.3.3 RNA seqFISH+
		7.3.4 Bioinformatics, image analysis, and visualization tools
	7.4 Applications and impact of spatial transcriptomics
		7.4.1 Developmental biology
		7.4.2 Neurobiology
		7.4.3 Skin biology
		7.4.4 Regeneration
	7.5 Perspectives
	References
8 Desert plant transcriptomics and adaptation to abiotic stress
	8.1 Introduction
	8.2 Potential of desert plant research
	8.3 Strategies for gene discovery in desert plants
	8.4 Current state of desert plant transcriptomics
	8.5 Drought stress
	8.6 Salinity stress
	8.7 Heat and cold stress
	8.8 Oxidative stress
	8.9 Identification of lncRNA as key regulators in adaptation to abiotic stress
		8.9.1 Identification of lncRNAs
		8.9.2 Downstream analysis of lncRNAs
	8.10 Conclusions and perspectives
	References
9 Transcriptomics in agricultural sciences: capturing changes in gene regulation during abiotic or biotic stress
	9.1 Application of transcriptomics in breeding
	9.2 Transcriptomics and plant interactions: from genes to the field
		9.2.1 Experimental design
		9.2.2 Sequencing
		9.2.3 Analysis
	9.3 Transcriptomics and breeding of orphan crops
	9.4 Transcriptomic technology for gene identification: expression regulation for biotic stress resistance, quality traits, ...
	9.5 Advances in transcriptomic analysis of multiple abiotic stresses
	9.6 RNA-seq coupled with other genomic tools in agricultural sciences: multiomics technologies to study metabolism during m...
	References
10 Transcriptomics in response of biotic stress in plants
	10.1 Introduction
	10.2 Methodology of RNA-seq analysis
	10.3 Transcriptome analysis of biotic stress response in crop plants
		10.3.1 Transcriptome studies of plant−fungus interactions
		10.3.2 Transcriptomics of plant−oomycetes interaction
		10.3.3 Transcriptomics of plant−bacteria interaction
		10.3.4 Transcriptomics for virus−plant interaction
		10.3.5 Transcriptome analysis for other biotic stresses
	10.4 Conclusion
	References
11 Functional genomics to understand the tolerance mechanism against biotic and abiotic stresses in Capsicum species
	11.1 Introduction
	11.2 Economic and medicinal importance of Capsicum
	11.3 Impact of stresses on Capsicum
	11.4 Application of omics tools towards understanding the plant responses against various stresses and their tolerance mech...
		11.4.1 Genomics
		11.4.2 Transcriptomics
		11.4.3 Proteomics
		11.4.4 Metabolomics
		11.4.5 Ionomics
		11.4.6 Phosphoproteomics
		11.4.7 Integrated omics
	11.5 Functional genomics of biotic and abiotic stress responses in Capsicum
		11.5.1 Biotic stresses
			11.5.1.1 Bacteria
			11.5.1.2 Viruses
			11.5.1.3 Nematodes
			11.5.1.4 Fungi
		11.5.2 Abiotic stresses
			11.5.2.1 Heat
			11.5.2.2 Temperature
			11.5.2.3 Cold
			11.5.2.4 Salinity
			11.5.2.5 Nutrient stress
			11.5.2.6 Water stress: drought and submergence/waterlogging
			11.5.2.7 Heavy metal stress
			11.5.2.8 Light
	11.6 Developing stress-tolerant Capsicum cultivars
	11.7 Concluding remarks
	References
	Further reading
12 Transcriptomic and epigenomic network analysis reveals chicken physiological reactions against heat stress
	12.1 Introduction
	12.2 The importance of knowing nonadapted and adaptation-specific biological reaction mechanisms
	12.3 Strategy
	12.4 Comparison of two chicken heart and muscle transcriptome datasets
		12.4.1 Datasets
		12.4.2 Comparison of transcriptomic datasets 1 and 2
			12.4.2.1 Heart tissue highland chicken
			12.4.2.2 Heart tissue, the difference between highland and lowland chicken
			12.4.2.3 Heart tissue lowland chicken
			12.4.2.4 Muscle tissue highland chicken
			12.4.2.5 Muscle tissue, the difference between highland and lowland chicken
			12.4.2.6 Muscle tissue lowland chicken
	12.5 Comparison of transcriptome and epigenome datasets
		12.5.1 Dataset 3: Epigenome analysis of heart tissue of experimentally induced heat stress in chicken eggs
			12.5.1.1 Lowland chicken heart transcriptomics versus embryonic heart epigenomics
			12.5.1.2 Highland chicken heart transcriptomics versus embryonic heart epigenomics
			12.5.1.3 Heart tissue difference between highland and lowland chicken versus embryonic heart epigenomics
	12.6 General reactions of adapted and not-adapted chicken types to heat stress
	12.7 Conclusions
	Perspectives
	References
13 Transcriptome-wide identification of immune-related genes after bacterial infection in fish
	13.1 Introduction
	13.2 Importance of transcriptome in aquaculture
	13.3 Concept of transcriptome workflow in fish
	13.4 Fish immune response post bacterial infection
	13.5 Conclusion
	References
14 Human transcriptome profiling: applications in health and disease
	14.1 Introduction
	14.2 A brief history of transcriptomics
	14.3 Microarrays
		14.3.1 Principles and progress
		14.3.2 Methods
		14.3.3 Applications of microarray in drug discovery
	14.4 RNA-seq
		14.4.1 Principles and progress
		14.4.2 Methods
		14.4.3 Data analysis using RNA-Seq
			14.4.3.1 Quality control
			14.4.3.2 Alignment
			14.4.3.3 Quantification
			14.4.3.4 Differential expression
			14.4.3.5 Validation
		14.4.4 Advantages of RNA-seq technology
		14.4.5 Applications of RNA-Seq in drug discovery
	14.5 Single-cell transcriptomics
		14.5.1 Applications of single-cell transcriptomics
	14.6 Conclusion and future perspectives
	References
15 Transcriptomics to devise human health and disease
	15.1 Introduction
	15.2 Transcriptomics
		15.2.1 Transcriptome profiling using microarrays
		15.2.2 Next-generation RNA sequencing
		15.2.3 Single-cell and spatial transcriptomics
	15.3 Transcriptomics of noncoding RNAs
	15.4 Application of transcriptomics
	15.5 System biology: integration of omics
	15.6 Conclusions
	References
16 Single-cell/nucleus transcriptomic and muscle pathologies
	16.1 Methods and technologies for single-cell/nucleus RNA sequencing
		16.1.1 Validation of scRNA-seq results
	16.2 Advantages and disadvantages of using single-cell/nucleus analysis
	16.3 Different muscles and different functions
		16.3.1 Heart
		16.3.2 Skeletal muscle
		16.3.3 Smooth muscle
	16.4 Single-cell/nucleus analysis in skeletal muscle. What does the dimension of the cells (myofibers) allow or not allow t...
	16.5 Single-cell/nucleus analysis in heart
	16.6 Single-cell analysis in smooth muscles
	16.7 Single-cell/nucleus RNA-seq bioinformatics analysis
	16.8 Discussion and conclusions
	References
17 Transcriptomics of intracranial aneurysms
	17.1 Introduction
	17.2 Intracranial aneurysms
	17.3 Transcriptomics of intracranial aneurysms
	17.4 Transcriptomics of unruptured and ruptured intracranial aneurysms
	17.5 Blood transcriptomic fingerprints of intracranial aneurysms
	17.6 Immune cell transcriptomic fingerprints of intracranial aneurysms
	17.7 Concluding remarks
	References
18 Recent advances in transcriptomic biomarker detection for cancer
	18.1 Introduction
	18.2 The evolution of transcriptomic methods
		18.2.1 Microarray
		18.2.2 RNA seq
		18.2.3 Computational analysis of RNA seq data
		18.2.4 Differential gene expression analysis and its application in cancer biomarker detection
	18.3 Cancer biomarkers currently in clinical use
		18.3.1 Breast cancer
		18.3.2 Lung cancer
		18.3.3 Colon and rectal cancer
		18.3.4 Liver cancer
	18.4 Steps of clinical biomarker development in cancer
		18.4.1 Preclinical research and biomarker discovery
		18.4.2 Validation of cancer biomarker and assay development for clinical use
		18.4.3 Retrospective analysis and validation of clinical significance
		18.4.4 Cancer control studies
	18.5 Cancer data availability in the form of database
	18.6 Application of machine learning in biomarker identification
		18.6.1 Feature selection algorithms
		18.6.2 Classification algorithms
			18.6.2.1 Support vector machine
			18.6.2.2 Artificial neural networks
			18.6.2.3 Decision trees
			18.6.2.4 Random forest
			18.6.2.5 Ensemble approach
	18.7 Conclusion
	References
19 Future prospects of transcriptomics
	19.1 Transcriptome: regulatory mechanisms
	19.2 Current perspectives in the field of transcriptomics and health
	19.3 Translational transcriptomics of cancer
	19.4 Translational transcriptomics of obesity
	19.5 Epitranscriptomics
	19.6 Types of significant RNA modifications
	19.7 Final considerations
	References
Index
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