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RNA-seq Data Analysis_ A Practical Approach (Chapman & Hall_CRC Mlainen & Ja.pdf
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目录
Contents
Preface, xvii
Acknowledgments, xxi
Authors, xxiii
Chapter 1 ◾ Introduction to RNA-seq 1
1.1 INTRODUCTION 1
1.2 ISOLATION OF RNAs 3
1.3 QUALITY CONTROL OF RNA 4
1.4 LIBRARY PREPARATION 6
1.5 MAJOR RNA-SEQ PLATFORMS 9
1.5.1 Illumina 9
1.5.2 SOLID 10
1.5.3 Roche 454 11
1.5.4 Ion Torrent 11
1.5.5 Pacific Biosciences 12
1.5.6 Nanopore Technologies 13
1.6 RNA-SEQ APPLICATIONS 14
1.6.1 Protein Coding Gene Structure 14
1.6.2 Novel Protein-Coding Genes 16
1.6.3 Quantifying and Comparing Gene Expression 16
1.6.4 Expression Quantitative Train Loci (eQTL) 17
1.6.5 Single-Cell RNA-seq 18
1.6.6 Fusion Genes 18
viii ◾ Contents
1.6.7 Gene Variations 19
1.6.8 Long Noncoding RNAs 19
1.6.9 Small Noncoding RNAs (miRNA-seq) 20
1.6.10 Amplification Product Sequencing (Ampli-seq) 20
1.7 CHOOSING AN RNA-SEQ PLATFORM 21
1.7.1 Eight General Principles for Choosing an RNA-seq Platform and Mode of Sequencing 21
1.7.1.1 Accuracy: How Accurate Must the Sequencing Be? 21
1.7.1.2 Reads: How Many Do I Need? 22
1.7.1.3 Length: How Long Must the Reads Be? 23
1.7.1.4 SR or PE: Single Read or Paired End? 23
1.7.1.5 RNA or DNA: Am I Sequencing RNA or DNA? 23
1.7.1.6 Material: How Much Sample Material Do I Have? 24
1.7.1.7 Costs: How Much Can I Spend? 24
1.7.1.8 Time: When Does the Work Need to Be Completed? 24
1.7.2 Summary 25
REFERENCES 25
Chapter 2 ◾ Introduction to RNA-seq Data Analysis 27
2.1 INTRODUCTION 27
2.2 DIFFERENTIAL EXPRESSION ANALYSIS WORKFLOW 30
2.2.1 Step 1: Quality Control of Reads 31
2.2.2 Step 2: Preprocessing of Reads 31
2.2.3 Step 3: Aligning Reads to a Reference Genome 31
2.2.4 Step 4: Genome-Guided Transcriptome Assembly 32
2.2.5 Step 5: Calculating Expression Levels 32
2.2.6 Step 6: Comparing Gene Expression between Conditions 33
2.2.7 Step 7: Visualization of Data in Genomic Context 33
Contents ◾ ix
2.3 DOWNSTREAM ANALYSIS 34
2.3.1 Gene Annotation 34
2.3.2 Gene Set Enrichment Analysis 34
2.4 AUTOMATED WORKFLOWS AND PIPELINES 35
2.5 HARDWARE REQUIREMENTS 35
2.6 FOLLOWING THE EXAMPLES IN THE BOOK 36
2.6.1 Using Command Line Tools and R 36
2.6.2 Using the Chipster Software 37
2.6.3 Example Data Sets 39
2.7 SUMMARY 40
REFERENCES 40
Chapter 3 ◾ Quality Control and Preprocessing 41
3.1 INTRODUCTION 41
3.2 SOFTWARE FOR QUALITY CONTROL AND PREPROCESSING 42
3.2.1 FastQC 42
3.2.2 PRINSEQ 43
3.2.3 Trimmomatic 44
3.3 READ QUALITY ISSUES 44
3.3.1 Base Quality 44
3.3.1.1 Filtering 45
3.3.1.2 Trimming 49
3.3.2 Ambiguous Bases 52
3.3.3 Adapters 54
3.3.4 Read Length 55
3.3.5 Sequence-Specific Bias and Mismatches Caused by Random Hexamer Priming 56
3.3.6 GC Content 57
3.3.7 Duplicates 57
3.3.8 Sequence Contamination 59
3.3.9 Low-Complexity Sequences and PolyA Tails 59x ◾ Contents
3.4 SUMMARY 60
REFERENCES 61
Chapter 4 ◾ Aligning Reads to Reference 63
4.1 INTRODUCTION 63
4.2 ALIGNMENT PROGRAMS 64
4.2.1 Bowtie 64
4.2.2 TopHat 68
4.2.3 STAR 73
4.3 ALIGNMENT STATISTICS AND UTILITIES FOR MANIPULATING ALIGNMENT FILES 77
4.4 VISUALIZING READS IN GENOMIC CONTEXT 81
4.5 SUMMARY 82
REFERENCES 83
Chapter 5 ◾ Transcriptome Assembly 85
5.1 INTRODUCTION 85
5.2 METHODS 87
5.2.1 Transcriptome Assembly Is Different from Genome Assembly 87
5.2.2 Complexity of Transcript Reconstruction 88
5.2.3 Assembly Process 89
5.2.4 de Bruijn Graph 90
5.2.5 Use of Abundance Information 91
5.3 DATA PREPROCESSING 92
5.3.1 Read Error Correction 93
5.3.2 Seecer 93
5.4 MAPPING-BASED ASSEMBLY 95
5.4.1 Cufflinks 95
5.4.2 Scripture 97
5.5 DE NOVO ASSEMBLY 98
5.5.1 Velvet + Oases 98
5.5.2 Trinity 100Contents ◾ xi
5.6 SUMMARY 104
REFERENCES 106
Chapter 6 ◾ Quantitation and Annotation-Based Quality Control 109
6.1 INTRODUCTION 109
6.2 ANNOTATION-BASED QUALITY METRICS 110
6.2.1 Tools for Annotation-Based Quality Control 111
6.3 QUANTITATION OF GENE EXPRESSION 116
6.3.1 Counting Reads per Genes 117
6.3.1.1 HTSeq 117
6.3.2 Counting Reads per Transcripts 120
6.3.2.1 Cufflinks 122
6.3.2.2 eXpress 122
6.3.3 Counting Reads per Exons 126
6.4 SUMMARY 128
REFERENCES 129
Chapter 7 ◾ RNA-seq Analysis Framework in R and Bioconductor 131
7.1 INTRODUCTION 131
7.1.1 Installing R and Add-on Packages 132
7.1.2 Using R 133
7.2 OVERVIEW OF THE BIOCONDUCTOR PACKAGES 134
7.2.1 Software Packages 134
7.2.2 Annotation Packages 134
7.2.3 Experiment Packages 135
7.3 DESCRIPTIVE FEATURES OF THE BIOCONDUCTOR PACKAGES 135
7.3.1 OOP Features in R 135
7.4 REPRESENTING GENES AND TRANSCRIPTS IN R 138
7.5 REPRESENTING GENOMES IN R 141
7.6 REPRESENTING SNPs IN R 143xii ◾ Contents
7.7 FORGING NEW ANNOTATION PACKAGES 143
7.8 SUMMARY 146
REFERENCES 146
Chapter 8 ◾ Differential Expression Analysis 147
8.1 INTRODUCTION 147
8.2 TECHNICAL VS. BIOLOGICAL REPLICATES 148
8.3 STATISTICAL DISTRIBUTIONS IN RNA-SEQ DATA 149
8.3.1 Biological Replication, Count Distributions, and Choice of Software 150
8.4 NORMALIZATION 152
8.5 SOFTWARE USAGE EXAMPLES 154
8.5.1 Using Cuffdiff 154
8.5.2 Using Bioconductor Packages: DESeq, edgeR, limma 158
8.5.3 Linear Models, the Design Matrix, and the Contrast Matrix 158
8.5.3.1 Design Matrix 159
8.5.3.2 Contrast Matrix 160
8.5.4 Preparations Ahead of Differential Expression Analysis 161
8.5.4.1 Starting from BAM Files 162
8.5.4.2 Starting from Individual Count Files 162
8.5.4.3 Starting from an Existing Count Table 163
8.5.4.4 Independent Filtering 163
8.5.5 Code Example for DESeq(2) 163
8.5.6 Visualization 164
8.5.7 For Reference: Code Examples for Other Bioconductor Packages 168
8.5.8 Limma 169
8.5.9 SAMSeq (samr package) 170
8.5.10 edgeR 171Contents ◾ xiii
8.5.11 DESeq2 Code Example for a Multifactorial Experiment 171
8.5.12 For Reference: edgeR Code Example 174
8.5.13 Limma Code Example 175
8.6 SUMMARY 176
REFERENCES 177
Chapter 9 ◾ Analysis of Differential Exon Usage 181
9.1 INTRODUCTION 181
9.2 PREPARING THE INPUT FILES FOR DEXSeq 183
9.3 READING DATA IN TO R 184
9.4 ACCESSING THE ExonCountSet OBJECT 185
9.5 NORMALIZATION AND ESTIMATION OF THE VARIANCE 187
9.6 TEST FOR DIFFERENTIAL EXON USAGE 190
9.7 VISUALIZATION 193
9.8 SUMMARY 198
REFERENCES 198
Chapter 10 ◾ Annotating the Results 199
10.1 INTRODUCTION 199
10.2 RETRIEVING ADDITIONAL ANNOTATIONS 200
10.2.1 Using an Organism-Specific Annotation Package to Retrieve Annotations for Genes 201
10.2.2 Using BioMart to Retrieve Annotations for Genes 205
10.3 USING ANNOTATIONS FOR ONTOLOGICAL ANALYSIS OF GENE SETS 208
10.4 GENE SET ANALYSIS IN MORE DETAIL 210
10.4.1 Competitive Method Using GOstats Package 211
10.4.2 Self-Contained Method Using Globaltest Package 213
10.4.3 Length Bias Corrected Method 215
10.5 SUMMARY 216
REFERENCES 216xiv ◾ Contents
Chapter 11 ◾ Visualization 217
11.1 INTRODUCTION 217
11.1.1 Image File Types 218
11.1.2 Image Resolution 218
11.1.3 Color Models 219
11.2 GRAPHICS IN R 219
11.2.1 Heatmap 220
11.2.2 Volcano Plot 224
11.2.3 MA Plot 226
11.2.4 Idiogram 228
11.2.5 Visualizing Gene and Transcript Structures 230
11.3 FINALIZING THE PLOTS 232
11.4 SUMMARY 234
REFERENCES 235
Chapter 12 ◾ Small Noncoding RNAs 237
12.1 INTRODUCTION 237
12.2 MICRORNAs (miRNAs) 239
12.3 MICRORNA OFF-SET RNAS (moRNAs) 243
12.4 PIWI-ASSOCIATED RNAS (piRNAs) 243
12.5 ENDOGENOUS SILENCING RNAs (endo-siRNAs) 244
12.6 EXOGENOUS SILENCING RNAs (exo-siRNAs) 244
12.7 TRANSFER RNAs (tRNAs) 245
12.8 SMALL NUCLEOLAR RNAs (snoRNAs) 245
12.9 SMALL NUCLEAR RNAs (snRNAs) 245
12.10 ENHANCER-DERIVED RNAs (eRNA) 246
12.11 OTHER SMALL NONCODING RNAs 246
12.12 SEQUENCING METHODS FOR DISCOVERY OF SMALL NONCODING RNAs 248
12.12.1 microRNA-seq 248
12.12.2 CLIP-seq 251
12.12.3 Degradome-seq 254
12.12.4 Global Run-On Sequencing (GRO-seq) 254Contents ◾ xv
12.13 SUMMARY 255
REFERENCES 255
Chapter 13 ◾ Computational Analysis of Small Noncoding RNA Sequencing Data 259
13.1 INTRODUCTION 259
13.2 DISCOVERY OF SMALL RNAs—miRDeep2 260
13.2.1 GFF files 260
13.2.2 FASTA Files of Known miRNAs 263
13.2.3 Setting up the Run Environment 263
13.2.4 Running miRDeep2 266
13.2.4.1 miRDeep2 Output 266
13.3 miRANALYZER 268
13.3.1 Running miRanalyzer 271
13.4 miRNA TARGET ANALYSIS 271
13.4.1 Computational Prediction Methods 272
13.4.2 Artificial Intelligence Methods 274
13.4.3 Experimental Support-Based Methods 275
13.5 miRNA-SEQ AND mRNA-SEQ DATA INTEGRATION 276
13.6 SMALL RNA DATABASES AND RESOURCES 277
13.6.1 RNA-seq Reads of miRNAs in miRBase 277
13.6.2 Expression Atlas of miRNAs 279
13.6.3 Database for CLIP-seq and Degradome-seq Data 281
13.6.4 Databases for miRNAs and Disease 281
13.6.5 General Databases for the Research Community and Resources 282
13.6.6 miRNAblog 282
13.7 SUMMARY 284
REFERENCES 284
INDEX 287
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