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Next generation sequencing data analysis / Xinkun Wang. -- Second edition -- Boca Raton : CRC Press, 2023. – (58.17115/W246/2nd ed.) |
Contents
Preface to the Second Edition
Author
Part I Introduction to Cellular and Molecular Biology
1. The Cellular System and the Code of Life
1.1 The Cellular Challenge
1.2 How Cells Meet the Challenge
1.3 Molecules in Cells
1.4 Intracellular Structures or Spaces
1.5 The Cell as a System
2. DNA Sequence: The Genome Base
2.1 The DNA Double Helix and Base Sequence
2.2 How DNA Molecules Replicate and Maintain Fidelity
2.3 How the Genetic Information Stored in DNA Is Transferred to Protein
2.4 The Genomic Landscape
2.5 DNA Packaging, Sequence Access, and DNA-Protein Interactions
2.6 DNA Sequence Mutation and Polymorphism
2.7 Genome Evolution
2.8 Epigenome and DNA Methylation
2.9 Genome Sequencing and Disease Risk
3. RNA: The Transcribed Sequence
3.1 RNA as the Messenger
3.2 The Molecular Structure of RNA
3.3 Generation, Processing, and Turnover of RNA as a Messenger
3.4 RNA Is More Than a Messenger
3.5 The Cellular Transcriptional Landscape
Part II Introduction to Next-Generation Sequencing (NGS) and NGS Data Analysis
4. Next-Generation Sequencing (NGS) Technologies: Ins and Outs
4.1 How to Sequence DNA: From First Generation to the Next
4.2 Ins and Outs of Different NGS Platforms
4.3 A Typical NGS Workflow
4.4 Biases and Other Adverse Factors That May Affect NGS Data Accuracy
4.5 Major Applications of NGS
5. Early-Stage Next-Generation Sequencing (NGS) Data Analysis: Common Steps
5.1 Basecalling, FASTQ File Format, and Base Quality Score
5.2 NGS Data Quality Control and Preprocessing
5.3 Read Mapping
5.4 Tertiary Analysis
6. Computing Needs for Next-Generation Sequencing (NGS)
Data Management and Analysis
6.1 NGS Data Storage, Transfer, and Sharing
6.2 Computing Power Required for NGS Data Analysis
6.3 Cloud Computing
6.4 Software Needs for NGS Data Analysis
6.5 Bioinformatics Skills Required for NGS Data Analysis
Part III Application-Specific NGS Data Analysis
7. Transcriptomics by Bulk RNA-Seq
7.1 Principle of RNA-Seq
7.2 Experimental Design
7.3 RNA-Seq Data Analysis
7.4 Visualization of RNA-Seq Data
7.5 RNA-Seq as a Discovery Tool
8. Transcriptomics by Single-Cell RNA-Seq
8.1 Experimental Design
8.2 Single-Cell Preparation, Library Construction, and Sequencing
8.3 Preprocessing of scRNA-Seq Data
8.4 Feature Selection, Dimension Reduction, and Visualization
8.5 Cell Clustering, Cell Identity Annotation, and Compositional Analysis
8.6 Differential Expression Analysis
8.7 Trajectory Inference
8.8 Advanced Analyses
9. Small RNA Sequencing
9.1 Small RNA NGS Data Generation and Upstream Processing
9.2 Identification of Differentially Expressed Small RNAs
9.3 Functional Analysis of Identified Known Small RNAs
10. Genotyping and Variation Discovery by Whole Genome/Exome Sequencing
10.1 Data Preprocessing, Mapping, Realignment, and Recalibration
10.2 Single Nucleotide Variant (SNV) and Short Indel Calling
10.3 Structural Variant (SV) Calling
10.4 Annotation of Called Variants
11. Clinical Sequencing and Detection of Actionable Variants
11.1 Clinical Sequencing Data Generation
11.2 Read Mapping and Variant Calling
11.3 Variant Filtering
11.4 Variant Ranking and Prioritization
11.5 Classification of Variants Based on Pathogenicity
11.6 Clinical Review and Reporting
11.7 Bioinformatics Pipeline Validation
12. De Novo Genome Assembly with Long and/or Short Reads
12.1 Genomic Factors and Sequencing Strategies for De Novo Assembly
12.2 Assembly of Contigs
12.3 Scaffolding and Gap Closure
12.4 Assembly Quality Evaluation
12.5 Limitations and Future Development
13. Mapping Protein-DNA Interactions with ChIP-Seq
13.1 Principle of ChIP-Seq
13.2 Experimental Design
13.3 Read Mapping, Normalization, and Peak Calling
13.4 Differential Binding Analysis
13.5 Functional Analysis
13.6 Motif Analysis
13.7 Integrated ChIP-Seq Data Analysis
14. Epigenomics by DNA Methylation Sequencing
14.1 DNA Methylation Sequencing Strategies
14.2 DNA Methylation Sequencing Data Analysis
14.3 Detection of Differentially Methylated Cytosines and Regions
14.4 Data Verification, Validation, and Interpretation
15. Whole Metagenome Sequencing for Microbial Community Analysis
15.1 Experimental Design and Sample Preparation
15.2 Sequencing Approaches
15.3 Overview of Shotgun Metagenome Sequencing Data Analysis
15.4 Sequencing Data Quality Control and Preprocessing
15.5 Taxonomic Characterization of a Microbial Community
15.6 Functional Characterization of a Microbial Community
15.7 Comparative Metagenomic Analysis
15.8 Integrated Metagenomics Data Analysis Pipelines
15.9 Metagenomics Data Repositories
Part IV The Changing Landscape of NGS Technologies and Data Analysis
16. What's Next for Next-Generation Sequencing (NGS)?
16.1 The Changing Landscape of Next-Generation Sequencing (NGS)
16.2 Newer Sequencing Technologies
16.3 Continued Evolution and Growth of Bioinformatics Tools for NGS Data Analysis
16.4 Efficient Management of NGS Analytic Workflows
16.5 Deepening Applications of NGS to Single-Cell and Spatial Sequencing
16.6 Increasing Use of Machine Learning in NGS Data Analytics
Appendix I Common File Types Used in NGS Data Analysis.
Appendix II Glossary
Index
