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Next-Generation Sequencing Data Analysis, 2nd Edition

Name: Next-Generation Sequencing Data Analysis, 2nd Edition
Author: Xinkun Wang
ISBN: 9781000897197

by Xinkun Wang

July 2023

Intermediate to advanced

434 pages

15h 16m

English

CRC Press

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Cover
Half Title
Title Page
Copyright Page
Table of Contents (1/2)
Table of Contents (2/2)
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 (1/2)
1.4.1 Nucleus1.4.2 Cell Membrane1.4.3 Cytoplasm1.4.4 Endosome, Lysosome, and Peroxisome1.4.5 Ribosome1.4.6 Endoplasmic Reticulum1.4.7 Golgi Apparatus
1.4 Intracellular Structures Or Spaces (2/2)
1.4.8 Cytoskeleton1.4.9 Mitochondrion
1.4.10 Chloroplast
1.5 The Cell as a System1.5.1 The Cellular System1.5.2 Systems Biology of the Cell1.5.3 How to Study the Cellular System
References
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
2.4.1 The Minimal Genome2.4.2 Genome Sizes2.4.3 Protein-Coding Regions of the Genome2.4.4 Non-Coding Genomic Elements
2.5 DNA Packaging, Sequence Access, and DNA-Protein Interactions
2.5.1 DNA Packaging2.5.2 Sequence Access2.5.3 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
2.9.1 Mendelian (Single-Gene) Diseases2.9.2 Complex Diseases That Involve Multiple Genes2.9.3 Diseases Caused By Genome Instability2.9.4 Epigenomic/Epigenetic Diseases
References
3 RNA: The Transcribed Sequence
3.1 RNA as the Messenger3.2 The Molecular Structure of RNA
3.3 Generation, Processing, and Turnover of RNA as a Messenger (1/2)
3.3.1 DNA Template3.3.2 Transcription of Prokaryotic Genes3.3.3 Pre-MRNA Transcription of Eukaryotic Genes3.3.4 Maturation of MRNA
3.3 Generation, Processing, and Turnover of RNA as a Messenger (2/2)
3.3.5 Transport and Localization3.3.6 Stability and Decay3.3.7 Major Steps of MRNA Transcript Level Regulation
3.4 RNA Is More Than a Messenger (1/2)
3.4.1 Ribozyme3.4.2 SnRNA and SnoRNA3.4.3 RNA for Telomere Replication3.4.4 RNAi and Small Non-Coding RNAs3.4.4.1 MiRNA
3.4 RNA Is More Than a Messenger (2/2)
3.4.4.2 SiRNA3.4.4.3 PiRNA3.4.5 Long Non-Coding RNAs3.4.6 Other Non-Coding RNAs
3.5 The Cellular Transcriptional Landscape
References
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 (1/3)
4.2.1 Illumina Reversible Terminator Short-Read Sequencing4.2.1.1 Sequencing Principle4.2.1.2 Implementation4.2.1.3 Error Rate, Read Length, Data Output, and Cost4.2.1.4 Sequence Data Generation4.2.2 Pacific Biosciences Single-Molecule Real-Time (SMRT) Long-Read Sequencing4.2.2.1 Sequencing Principle4.2.2.2 Implementation
4.2 Ins and Outs of Different NGS Platforms (2/3)
4.2.2.3 Error Rate, Read Length, Data Output, and Cost4.2.2.4 Sequence Data Generation4.2.3 Oxford Nanopore Technologies (ONT) Long-Read Sequencing4.2.3.1 Sequencing Principle4.2.3.2 Implementation4.2.3.3 Error Rate, Read Length, Data Output, and Cost4.2.3.4 Sequence Data Generation4.2.4 Ion Torrent Semiconductor Sequencing4.2.4.1 Sequencing Principle
4.2 Ins and Outs of Different NGS Platforms (3/3)
4.2.4.2 Implementation4.2.4.3 Error Rate, Read Length, Date Output, and Cost
4.2.4.4 Sequence Data Generation
4.3 A Typical NGS Workflow
4.4 Biases and Other Adverse Factors That May Affect NGS Data Accuracy
4.4.1 Biases in Library Construction4.4.2 Biases and Other Factors in Sequencing
4.5 Major Applications of NGS
4.5.1 Transcriptomic Profiling (Bulk and Single-Cell RNA-Seq)4.5.2 Genetic Mutation and Variation Identification4.5.3 De Novo Genome Assembly4.5.4 Protein-DNA Interaction Analysis (ChIP-Seq)4.5.5 Epigenomics and DNA Methylation Study (Methyl-Seq)
4.5.6 Metagenomics
References
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 (1/3)
5.3.1 Mapping Approaches and Algorithms
5.3 Read Mapping (2/3)
5.3.2 Selection of Mapping Algorithms and Reference Genome Sequences5.3.3 SAM/BAM as the Standard Mapping File Format5.3.4 Mapping File Examination and Operation
5.3 Read Mapping (3/3)
5.4 Tertiary Analysis
References
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
6.4.1 Parallel Computing
6.5 Bioinformatics Skills Required for NGS Data Analysis
References
Part III Application-Specific NGS Data Analysis
7 Transcriptomics By Bulk RNA-Seq
7.1 Principle of RNA-Seq
7.2 Experimental Design
7.2.1 Factorial Design7.2.2 Replication and Randomization7.2.3 Sample Preparation and Sequencing Library Preparation7.2.4 Sequencing Strategy
7.3 RNA-Seq Data Analysis (1/3)
7.3.1 Read Mapping7.3.2 Quantification of Reads
7.3 RNA-Seq Data Analysis (2/3)
7.3.3 Normalization7.3.4 Batch Effect Removal7.3.5 Identification of Differentially Expressed Genes
7.3 RNA-Seq Data Analysis (3/3)
7.3.6 Multiple Testing Correction7.3.7 Gene Clustering7.3.8 Functional Analysis of Identified Genes7.3.9 Differential Splicing Analysis
7.4 Visualization of RNA-Seq Data
7.5 RNA-Seq as a Discovery Tool
References (1/2)
References (2/2)
8 Transcriptomics By Single-Cell RNA-Seq
8.1 Experimental Design
8.1.1 Single-Cell RNA-Seq General Approaches8.1.2 Cell Number and Sequencing Depth8.1.3 Batch Effects Minimization and Sample Replication
8.2 Single-Cell Preparation, Library Construction, and Sequencing
8.2.1 Single-Cell Preparation8.2.2 Single Nuclei Preparation8.2.3 Library Construction and Sequencing
8.3 Preprocessing of ScRNA-Seq Data (1/3)
8.3.1 Initial Data Preprocessing and Quality Control8.3.2 Alignment and Transcript Counting8.3.3 Data Cleanup Post Alignment
8.3 Preprocessing of ScRNA-Seq Data (2/3)
8.3.4 Normalization8.3.5 Batch Effects Correction
8.3 Preprocessing of ScRNA-Seq Data (3/3)
8.3.6 Signal Imputation
8.4 Feature Selection, Dimension Reduction, and Visualization (1/2)
8.4.1 Feature Selection8.4.2 Dimension Reduction8.4.3 Visualization
8.4 Feature Selection, Dimension Reduction, and Visualization (2/2)
8.5 Cell Clustering, Cell Identity Annotation, and Compositional Analysis (1/2)
8.5.1 Cell Clustering8.5.2 Cell Identity Annotation
8.5 Cell Clustering, Cell Identity Annotation, and Compositional Analysis (2/2)
8.5.3 Compositional Analysis
8.6 Differential Expression Analysis
8.7 Trajectory Inference
8.8 Advanced Analyses
8.8.1 SNV/CNV Detection and Allele-Specific Expression Analysis8.8.2 Alternative Splicing Analysis8.8.3 Gene Regulatory Network Inference
References (1/4)
References (2/4)
References (3/4)
References (4/4)
9 Small RNA Sequencing
9.1 Small RNA NGS Data Generation and Upstream Processing
9.1.1 Data Generation9.1.2 Preprocessing9.1.3 Mapping9.1.4 Identification of Known and Putative Small RNA Species9.1.5 Normalization
9.2 Identification of Differentially Expressed Small RNAs
9.3 Functional Analysis of Identified Known Small RNAs
References
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 (1/2)
10.2.1 Germline SNV and Indel Calling10.2.2 Somatic Mutation Detection10.2.3 Variant Calling From RNA Sequencing Data10.2.4 Variant Call Format (VCF)
10.2 Single Nucleotide Variant (SNV) and Short Indel Calling (2/2)
10.2.5 Evaluating VCF Results
10.3 Structural Variant (SV) Calling
10.3.1 Short-Read-Based SV Calling10.3.2 Long-Read-Based SV Calling10.3.3 CNV Detection
10.3.4 Integrated SV Analysis
10.4 Annotation of Called Variants
References (1/2)
References (2/2)
11 Clinical Sequencing and Detection of Actionable Variants
11.1 Clinical Sequencing Data Generation
11.1.1 Patient Sample Collection11.1.2 Library Preparation and Sequencing Approaches
11.2 Read Mapping and Variant Calling
11.3 Variant Filtering11.3.1 Frequency of Occurrence11.3.2 Functional Consequence11.3.3 Existing Evidence of Relationship to Human Disease11.3.4 Clinical Phenotype Match
11.3.5 Mode of Inheritance
11.4 Variant Ranking and Prioritization
11.5 Classification of Variants Based On Pathogenicity (1/2)
11.5.1 Classification of Germline Variants
11.5 Classification of Variants Based On Pathogenicity (2/2)
11.5.2 Classification of Somatic Variants
11.6 Clinical Review and Reporting
11.6.1 Use of Artificial Intelligence in Variant Reporting11.6.2 Expert Review11.6.3 Generation of Testing Report11.6.4 Variant Validation11.6.5 Incorporation Into a Patient’s Electronic Health Record11.6.6 Reporting of Secondary Findings11.6.7 Patient Counseling and Periodic Report Updates
11.7 Bioinformatics Pipeline Validation
References (1/2)
References (2/2)
12 De Novo Genome Assembly With Long And/or Short Reads
12.1 Genomic Factors and Sequencing Strategies for De Novo Assembly
12.1.1 Genomic Factors That Affect De Novo Assembly12.1.2 Sequencing Strategies for De Novo Assembly
12.2 Assembly of Contigs (1/2)
12.2.1 Sequence Data Preprocessing, Error Correction, and Assessment of Genome Characteristics12.2.2 Contig Assembly Algorithms
12.2 Assembly of Contigs (2/2)
12.2.3 Polishing
12.3 Scaffolding and Gap Closure
12.4 Assembly Quality Evaluation
12.5 Limitations and Future Development
References (1/2)
References (2/2)
13 Mapping Protein-DNA Interactions With ChIP-Seq
13.1 Principle of ChIP-Seq
13.2 Experimental Design
13.2.1 Experimental Control13.2.2 Library Preparation13.2.3 Sequencing Length and Depth13.2.4 Replication
13.3 Read Mapping, Normalization, and Peak Calling (1/3)
13.3.1 Data Quality Control and Read Mapping13.3.2 Peak Calling
13.3 Read Mapping, Normalization, and Peak Calling (2/3)
13.3 Read Mapping, Normalization, and Peak Calling (3/3)
13.3.3 Post-Peak Calling Quality Control13.3.4 Peak Visualization
13.4 Differential Binding Analysis
13.5 Functional Analysis
13.6 Motif Analysis
13.7 Integrated ChIP-Seq Data Analysis
References
14 Epigenomics By DNA Methylation Sequencing
14.1 DNA Methylation Sequencing Strategies14.1.1 Bisulfite Conversion Methyl-Seq14.1.1.1 Whole-Genome Bisulfite Sequencing (WGBS)14.1.1.2 Reduced Representation Bisulfite Sequencing (RRBS)14.1.2 Enzymatic Conversion Methyl-Seq14.1.3 Enrichment-Based Methyl-Seq14.1.4 Differentiation of Cytosine Methylation From Demethylation Products
14.2 DNA Methylation Sequencing Data Analysis (1/2)
14.2.1 Quality Control and Preprocessing14.2.2 Read Mapping14.2.3 Quantification of DNA Methylation/Demethylation Products
14.2 DNA Methylation Sequencing Data Analysis (2/2)
14.2.4 Visualization
14.3 Detection of Differentially Methylated Cytosines and Regions
14.4 Data Verification, Validation, and Interpretation
References (1/2)
References (2/2)
15 Whole Metagenome Sequencing for Microbial Community Analysis
15.1 Experimental Design and Sample Preparation
15.1.1 Metagenome Sample Collection15.1.2 Metagenome Sample Processing
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 Community15.5.1 Metagenome Assembly15.5.2 Sequence Binning15.5.3 Calling of Genes and Other Genomic Elements From Metagenomic Sequences15.5.4 Taxonomic Profiling
15.6 Functional Characterization of a Microbial Community
15.6.1 Gene Function Annotation15.6.2 Gene Function Profiling and Metabolic Pathway Reconstruction
15.7 Comparative Metagenomic Analysis
15.7.1 Metagenome Sequencing Data Normalization
15.7.2 Identification of Differentially Abundant Species Or OTUs
15.8 Integrated Metagenomics Data Analysis Pipelines15.9 Metagenomics Data Repositories
References (1/2)
References (2/2)
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
References (1/2)
References (2/2)
Appendix I Common File Types Used in NGS Data Analysis
Appendix II Glossary (1/2)
Appendix II Glossary (2/2)
Index (1/4)
Index (2/4)
Index (3/4)
Index (4/4)

Content preview from Next-Generation Sequencing Data Analysis, 2nd Edition

Next-Generation Sequencing Data Analysis132

Bayesian based methods, in which two alternative models are proposed

for each gene, with one assuming differential expression and another

assuming null. Given the observed counts, the posterior likelihood for the

differential expression model is used to identify DE genes. Cuffdiff 2 uses

the T statistic, which equals the ratio of mean(log[y] ) over variance(log[y]),

with y representing the expression ratio of a gene between two groups.

Since this statistic approximately follows a normal distribution, t- test is

used for DE analysis. DEGseq employs several methods to identify DE

genes, including ...

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Next-Generation Sequencing Data Analysis, 2nd Edition

by Xinkun Wang

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