Handbook of translational transcriptomics : research, protocols and applications / edited by Anton Buzdin.

Format:

Book

Contributor:

Buzdin, Anton, editor.

Language:

English

Subjects (All):

Bioinformatics.

Databases.

Physical Description:

1 online resource (771 pages)

Edition:

First edition.

Place of Publication:

San Diego, CA : Academic Press, [2025]

Summary:

Handbook of Translational Transcriptomics: Research, Protocols and Applications provides a comprehensive overview of the field of transcriptomics.With an emphasis on the various protocols and techniques available for investigation, it acts as a practical guide to researchers for implementing their own investigations in the field.This book begins.

Contents:

Front Cover

HANDBOOK OF TRANSLATIONAL TRANSCRIPTOMICS

HANDBOOK OF TRANSLATIONAL TRANSCRIPTOMICS: RESEARCH, PROTOCOLS AND APPLICATIONS

Copyright

Contents

Contributors

About the editor

Acknowledgments

1 - Past, current, and future of transcriptomics

1.1 Introduction

1.2 Historical development

1.3 Methodological advancements

1.4 Microarrays

1.5 RNA sequencing (RNA-seq)

1.6 Single-cell RNA sequencing (scRNA-seq)

1.7 Spatial transcriptomics

1.8 Noncoding RNAs in gene regulation

1.9 MicroRNAs (miRNAs)

1.10 Long noncoding RNAs (lncRNAs)

1.11 Regulatory networks

1.12 Single-cell and spatial transcriptomics in understanding cellular heterogeneity

1.13 Single-cell transcriptomics

1.14 Spatial transcriptomics

1.15 Insights and applications

1.16 Challenges and future directions

1.17 Emerging trends and future directions in transcriptomics

1.18 RNA sequencing (RNA-seq) and beyond

1.19 Dual RNA-seq for host-pathogen interactions

1.20 Transcriptomics in food microbiology and agriculture

1.21 Multi-omics integration

1.22 Single-cell transcriptomics

1.23 Challenges and future prospects

1.24 Current challenges and controversies in transcriptomics

1.25 Technical limitations and data complexity

1.26 Interpretation of dual RNA-seq data

1.27 Environmental influences on transcriptomic profiles

1.28 Transcriptomics in food microbiology and safety

1.29 Toxicogenomics and risk assessment

1.30 Functional annotation and novel transcripts

1.31 Conclusion

References

2 - Pitfalls of transcriptomics and selection of the most appropriate transcriptomic technique

2.1 Introduction

2.1.1 Understanding the limitations of transcriptomic data

2.2 Pitfalls in specific transcriptomic techniques

2.2.1 Single-cell RNA-seq: Coverage and detection limits.

2.2.2 Bulk RNA-seq: Averaging effects and loss of resolution in heterogeneous samples

2.2.3 Spatial transcriptomics: Challenges in resolution and data integration

2.2.4 Expression arrays: Hybridization challenges, background noise, and dynamic range limitations

2.2.5 High-throughput and multiplex PCR: Primer specificity issues and amplification biases

2.2.6 Navigating pitfalls in transcriptomic techniques

2.3 Choosing the right tool for the job

2.3.1 Decision trees for selecting transcriptomic techniques

2.3.2 Practical guide to navigating transcriptomic techniques

2.4 Case studies and examples

2.4.1 Real-world applications and pitfalls in transcriptomic research

2.4.2 Case study 1: Performance evaluation of transcriptomics data normalization for survival risk prediction

2.4.3 Case study 2: Power analysis of transcriptome-wide association study: Implications for practical protocol choice

2.4.4 Case study 3: Research progress and future development trends in medicinal plant transcriptomics

2.4.5 Case study 4: Exploring tissue architecture using spatial transcriptomics

2.5 Addressing pitfalls and challenges: Insights from recent research

2.5.1 Overcoming data interpretation challenges

2.5.2 Enhancing spatial transcriptomics

2.5.3 Robust transcript isoform discovery

2.5.4 Mitigating abundant RNA interference

2.5.5 Cell composition inference in spatial transcriptomics

2.5.6 AI-assisted transcriptomic analysis in cancer immunotherapy

2.6 Future directions in transcriptomics

2.6.1 Single-cell RNA sequencing

2.6.2 Artificial intelligence-assisted transcriptomic analysis

2.6.3 Spatial transcriptomics

2.6.4 High-affinity oligonucleotides for RNA sequencing

2.6.5 Integration with CRISPR technology

2.6.5.1 Emerging technologies and future trends in transcriptomics.

2.6.6 Enhanced diagnostic yield in rare diseases

2.6.7 Improved predictive models through deep learning

2.6.8 Increased interpretability in survival analysis

2.6.9 Construction of gene regulatory networks

2.6.10 Improved drug response predictions

2.6.11 Advancements in plant omics

2.6.12 Single-molecule approaches

2.6.13 Mechanistic insights into gene expression

2.6.14 Reconstruction of regulatory networks

2.6.15 Identification of epigenetic vulnerabilities

2.7 Practical considerations and conclusion

2.7.1 Sample preparation and quality control

2.7.2 Data analysis and bioinformatics

2.7.3 Integration with other biological data

2.7.4 Standardization and reproducibility

2.7.5 Ethical considerations

2.7.6 Interdisciplinary collaboration

2.7.7 Technological adaptation and evolution

2.7.8 Funding and resource allocation

2.7.9 Training and education

Further reading

3 - Bulk RNA sequencing in wet lab

3.1 Introduction to bulk RNA sequencing

3.1.1 Overview of RNA sequencing technologies

3.1.2 Historical perspective and evolution of bulk RNA sequencing

3.1.3 Importance of transcriptomics

3.2 Fundamental principles of bulk RNA sequencing

3.2.1 Bulk RNA sequencing

3.2.2 Single-cell RNA sequencing

3.2.3 Comparative insights

3.3 Key technologies and methodologies in bulk RNA sequencing

3.3.1 Library preparation

3.3.2 Sequencing technologies

3.3.3 Data analysis pipelines

3.3.4 Normalization and interpretation

3.4 Sample collection and storage

3.4.1 Sample collection

3.4.2 Storage conditions

3.4.3 RNA integrity assessment

3.5 RNA extraction and purification

3.5.1 Methodologies for RNA extraction

3.5.2 Purification and quality assessment

3.5.3 Considerations

3.6 Quality control measures

3.6.1 RNA purity assessment.

3.6.2 RNA integrity evaluation

3.6.3 Quantification of RNA

3.6.4 Assessment of genomic DNA contamination

3.6.5 Library quality and quantity verification

3.6.6 Considerations

3.7 Library preparation for bulk RNA sequencing

3.7.1 Overview of library preparation protocols

3.7.1.1 Poly-A selection versus ribosomal RNA depletion

3.7.1.2 Fragmentation and reverse transcription

3.7.1.3 Adapter ligation and PCR amplification

3.7.1.4 Quality control and validation

3.7.1.5 Considerations for library preparation

3.8 Early barcoding and cost efficiency

3.8.1 Principles of early barcoding

3.8.2 Cost efficiency of prime-seq

3.8.3 Comparison with standard methods

3.8.4 Implications for research

3.9 Direct RNA isolation strategies

3.9.1 Techniques for direct RNA isolation

3.9.2 Advantages of direct RNA isolation for RNA-seq library preparation

3.9.3 Considerations and best practices

3.10 Sequencing platforms and technologies

3.10.1 Comparison of sequencing platforms (Illumina, PacBio, Oxford Nanopore)

3.10.2 Advantages and limitations of sequencing platforms

3.10.3 Illumina or BGI sequencing

3.10.3.1 Advantages

3.10.3.2 Limitations

3.10.4 PacBio sequencing (SMRT)

3.10.4.1 Advantages

3.10.4.2 Limitations

3.10.5 Oxford nanopore technologies

3.10.5.1 Advantages

3.10.5.2 Limitations

3.11 RNA sequencing protocols

3.11.1 Standard RNA-seq protocol

3.11.2 Isoform sequencing (Iso-seq)

3.11.3 Single-molecule direct RNA sequencing

3.11.4 Targeted RNA sequencing

3.12 Overview of advanced RNA sequencing protocols

3.12.1 Prime-seq

3.12.2 STORM-seq for full-length single-cell and bulk RNA profiling

3.12.3 Novel protocols for full-length total RNA sequencing

3.13 Reagents and equipment for bulk RNA sequencing.

3.13.1 Variants of essential reagents and their suppliers

3.13.1.1 RNA extraction kits

3.13.2 Quality control instruments

3.13.3 Equipment and software requirements

3.13.3.1 Sequencing platforms

3.13.4 Computational infrastructure

3.13.5 Bioinformatics software

3.13.6 Laboratory equipment

3.14 Technical and analytical quality control in RNA sequencing

3.14.1 Presequencing quality control measures

3.14.1.1 RNA integrity and quality

3.14.2 Library quality

3.14.3 Contaminant checks

3.14.4 Post-sequencing QC and data validation

3.14.4.1 Sequencing output and quality

3.14.5 Data normalization and batch effects

3.14.6 Biological replication and technical reproducibility

3.15 Bioinformatics and data analysis

3.15.1 Overview of bioinformatics pipelines for bulk RNA sequencing

3.15.2 Standard pipeline stages

3.15.2.1 Quality control

3.15.3 Read mapping

3.15.4 Quantification of gene and transcript levels

3.15.5 Differential expression analysis

3.15.6 Functional enrichment analysis

3.15.7 Considerations for choosing a pipeline

3.16 Data preprocessing and normalization methods for bulk RNA sequencing

3.16.1 Data preprocessing

3.16.2 Normalization methods

3.17 Sequencing depth and coverage in bulk RNA sequencing

3.17.1 Recommendations for sequencing depth for different applications

3.17.1.1 Gene expression profiling

3.17.1.2 Transcriptome annotation and discovery of novel transcripts

3.17.1.3 Alternative splicing and isoform quantification

3.17.1.4 Single-cell RNA-seq (scRNA-seq)

3.17.2 Impact of sequencing depth on data quality and interpretation

3.17.2.1 Sensitivity and specificity

3.17.2.2 Quantitative accuracy

3.17.2.3 Coverage uniformity

3.17.2.4 Cost considerations

3.18 Differential expression analysis in RNA sequencing.

3.18.1 Methods for differential expression analysis.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

0-443-23768-9

0-443-19110-7

OCLC:

1519120778