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Transcriptome profiling : progress and prospects / edited by Mohammad Ajmal Ali and Joongku Lee. -- London, UK : Academic Press, 2023. – (58.1481/T772n) |
Contents
List of contributors xi
Preface xv
1. Transcriptomic analysis of genes: expression and regulation 1
1.1 Techniques for transcription analysis and RNA-seq profiling 2
1.2 Sequencing platforms and gene analysis workflow for genome and transcriptome assembly 7
1.3 Expression and differential expression analysis: methods and programs 11
1.4 Data integration techniques for coexpression network construction 15
1.5 Gene regulation studies on bacteria and fungi 18
1.6 Application of transcriptomics to the study of small RNAs, transcription factors, heat shock factors, kinases (MAPK), PCR, and metabolite production 20
1.7 Transcriptomic studies of genetic engineering approaches 22
1.8 Application of transcriptomics in the context of diseases and clinical studies 23
References 27
2. Transcriptomics and genetic engineering 43
2.1 Introduction 44
2.2 History 44
2.3 Transcriptomics 47
2.4 Gene ontology 49
2.5 Genetic engineering approaches to target the transcriptome 52
2.6 Model organisms for transcriptome research 54
2.7 Challenges and conclusion 56
Authors contributions 57
Financial support 57
Competing interests 57
Acknowledgements 58
References 58
Further reading 65
3. Single-cell transcriptomics 67
3.1 Introduction 67
3.2 Measurement techniques in single-cell transcriptomics 69
3.3 Noise in single-cell sequencing 71
3.4 Preprocessing of 10X scRNAseq data 72
3.5 Analysis of 10X scRNAseq data 75
References 81
4. Time course gene expression experiments 85
4.1 Introduction 85
4.2 Designing time course experiments 87
4.3 A holistic method to analyze time course gene expression experiments 89
4.4 Conclusions and perspectives 101
4.5 Appendix: standardized expression profile estimation 102
References 107
5. Measurement and meaning in gene expression evolution 111
5.1 Introduction 112
5.2 What is gene expression? 113
5.3 Gene expression evolution 115
5.4 Measuring gene expression 117
5.5 Measuring gene expression evolution 119
Acknowledgments 123
References 123
6. G-quadruplexes as key motifs in transcriptomics 131
6.1 Introduction 132
6.2 G-quadruplexes 132
6.3 Approaches to identify G4s 137
6.4 Functions of G4s 141
6.5 Genome instability associated to G4s 154
6.6 G4-binding proteins 155
6.7 G4s' involvement in disease 156
6.8 G4 Ligands 158
6.9 Future perspectives 160
References 161
7. Spatial transcriptomics 175
7.1 An introduction to spatial transcriptomics 175
7.2 Origin of spatial transcriptomics 177
7.3 Implementation of a spatial transcriptomics study: tools and techniques 178
7.4 Applications and impact of spatial transcriptomics 184
7.5 Perspectives 191
References 192
8. Desert plant transcriptomics and adaptation to abiotic stress 199
8.1 Introduction 200
8.2 Potential of desert plant research 201
8.3 Strategies for gene discovery in desert plants 203
8.4 Current state of desert plant transcriptomics 205
8.5 Drought stress 210
8.6 Salinity stress 214
8.7 Heat and cold stress 217
8.8 Oxidative stress 223
8.9 Identification of lncRNA as key regulators in adaptation to abiotic stress 225
8.10 Conclusions and perspectives 234
References 235
9. Transcriptomics in agricultural sciences: capturing changes in gene regulation during abiotic or biotic stress 257
9.1 Application of transcriptomics in breeding 258
9.2 Transcriptomics and plant interactions: from genes to the field 260
9.3 Transcriptomics and breeding of orphan crops 263
9.4 Transcriptomic technology for gene identification: expression regulation for biotic stress resistance, quality traits, signal transduction reactions, and defense responses 266
9.5 Advances in transcriptomic analysis of multiple abiotic stresses 269
9.6 RNA-seq coupled with other genomic tools in agricultural sciences: multiomics technologies to study metabolism during multiple stress responses 270
References 272
10. Transcriptomics in response of biotic stress in plants 285
10.1 Introduction 285
10.2 Methodology of RNA-seq analysis 286
10.3 Transcriptome analysis of biotic stress response in crop plants 287
10.4 Conclusion 294
References 295
11. Functional genomics to understand the tolerance mechanism against biotic and abiotic stresses in Capsicum species 305
11.1 Introduction 306
11.2 Economic and medicinal importance of Capsicum 307
11.3 Impact of stresses on Capsicum 307
11.4 Application of omics tools towards understanding the plant responses against various stresses and their tolerance mechanisms 308
11.5 Functional genomics of biotic and abiotic stress responses in Capsicum 314
11.6 Developing stress-tolerant Capsicum cultivars 318
11.7 Concluding remarks 320
References 320
Further reading 331
12. Transcriptomic and epigenomic network analysis reveals chicken physiological reactions against heat stress 333
12.1 Introduction 334
12.2 The importance of knowing nonadapted and adaptation-specific biological reaction mechanisms 336
12.3 Strategy 337
12.4 Comparison of two chicken heart and muscle transcriptome datasets 339
12.5 Comparison of transcriptome and epigenome datasets 350
12.6 General reactions of adapted and not-adapted chicken types to heat stress 352
12.7 Conclusions 355
Perspectives 356
Acknowledgment 356
References 356
13. Transcriptome-wide identification of immune-related genes after bacterial infection in fish 361
13.1 Introduction 361
13.2 Importance of transcriptome in aquaculture 362
13.3 Concept of transcriptome workflow in fish 363
13.4 Fish immune response post bacterial infection 366
13.5 Conclusion 369
References 369
14. Human transcriptome profiling: applications in health and disease 373
14.1 Introduction 373
14.2 A brief history of transcriptomics 375
14.3 Microarrays 376
14.4 RNA-seq 380
14.5 Single-cell transcriptomics 387
14.6 Conclusion and future perspectives 389
References 390
15. Transcriptomics to devise human health and disease 397
15.1 Introduction 398
15.2 Transcriptomics 398
15.3 Transcriptomics of noncoding RNAs 408
15.4 Application of transcriptomics 410
15.5 System biology: integration of omics 412
15.6 Conclusions 413
References 414
16. Single-cell/nucleus transcriptomic and muscle pathologies 419
16.1 Methods and technologies for single-cell/nucleus RNA sequencing 419
16.2 Advantages and disadvantages of using single-cell/nucleus analysis 422
16.3 Different muscles and different functions 424
16.4 Single-cell/nucleus analysis in skeletal muscle. What does the dimension of the cells (myofibers) allow or not allow to ? 426
16.5 Single-cell/nucleus analysis in heart 429
16.6 Single-cell analysis in smooth muscles 431
16.7 Single-cell/nucleus RNA-seq bioinformatics analysis 433
16.8 Discussion and conclusions 434
References 435
17. Transcriptomics of intracranial aneurysms 443
17.1 Introduction 443
17.2 Intracranial aneurysms 445
17.3 Transcriptomics of intracranial aneurysms 445
17.4 Transcriptomics of unruptured and ruptured intracranial aneurysms 447
17.5 Blood transcriptomic fingerprints of intracranial aneurysms 448
17.6 Immune cell transcriptomic fingerprints of intracranial aneurysms 448
17.7 Concluding remarks 449
References 449
18. Recent advances in transcriptomic biomarker detection for cancer 453
18.1 Introduction 454
18.2 The evolution of transcriptomic methods 454
18.3 Cancer biomarkers currently in clinical use 458
18.4 Steps of clinical biomarker development in cancer 461
18.5 Cancer data availability in the form of database 465
18.6 Application of machine learning in biomarker identification 466
18.7 Conclusion 470
References 471
19. Future prospects of transcriptomics 479
19.1 Transcriptome: regulatory mechanisms 479
19.2 Current perspectives in the field of transcriptomics and health 482
19.3 Translational transcriptomics of cancer 483
19.4 Translational transcriptomics of obesity 486
19.5 Epitranscriptomics 487
19.6 Types of significant RNA modifications 487
19.7 Final considerations 489
References 489
Index 493
