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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