RNA-seq data analysis: a practical approach
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL [und 2 weitere]
CRC Press
2015
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| Schriftenreihe: | Chapman & Hall/CRC mathematical & computational biology series
A Chapman & Hall book |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIV, 298 Seiten Diagramme |
| ISBN: | 9781466595002 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | Chapman amp; Hall/CRC Mathematical and Computational Biology Series
RNA-seq
Data Analysis
A Practical Approach
Eija Korpelainen
CSC - IT Center for Science
Espoo, Finland
JarnoTuimala
RS-koulutus
Helsinki, Finland
PanuSomervuo
University of Helsinki
Finland
SclLlfeLab, Stockholm University
Sweden
University of Eastern Finland
Kuopio, Finland
Taylor amp; Francis Group
Boca Raton London New York
CRC Press is an imprint of the
Taylor amp; Francis Group, an Informa business
ACHAPMAN amp;HALLBOOK
Mikael Huss
Garry Wong
CRC Press
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
151 Illumina 9
152 SOLID 10
153 Roche 454 11
154 Ion Torrent 11
155 Pacific Biosciences 12
156 Nanopore Technologies 13
1 6 RNA-SEQ APPLICATIONS 14
161 Protein Coding Gene Structure 14
162 Novel Protein-Coding Genes 16
163 Quantifying and Comparing Gene Expression 16
164 Expression Quantitative Train Loci (eQTL) 17
165 Single-Cell RNA-seq 18
166 Fusion Genes 18
viii • Contents
167 Gene Variations 19
168 Long Noncoding RNAs 19
169 Small Noncoding RNAs (miRNA-seq) 20
1 6 10 Amplification Product Sequencing (Ampli-seq) 20
1 7 CHOOSING AN RNA-SEQ PLATFORM 21
171 Eight General Principles for Choosing an RNA-seq
Platform and Mode of Sequencing 21
1711 Accuracy: How Accurate Must the
Sequencing Be? 21
1712 Reads: How Many Do I Need? 22
1713 Length: How Long Must the Reads Be? 23
1714 SR or PE: Single Read or Paired End? 23
1715 RNA or DNA: Am I Sequencing RNA or
DNA? 23
1716 Material: How Much Sample Material Do
1 I Have? 24
1717 Costs: How Much Can I Spend? 24
1718 Time: When Does the Work Need to Be
Completed? 24
172 Summary 25
REFERENCES 25
CHAPTER 2 • Introduction to RNA-seq Data Analysis 27
2 1 INTRODUCTION 27
2 2 DIFFERENTIAL EXPRESSION ANALYSIS WORKFLOW 30
221 Step 1: Quality Control of Reads 31
222 Step 2: Preprocessing of Reads 31
223 Step 3: Aligning Reads to a Reference Genome 31
224 Step 4: Genome-Guided Transcriptome Assembly 32
225 Step 5: Calculating Expression Levels 32
226 Step 6: Comparing Gene Expression between
Conditions 33
227 Step 7: Visualization of Data in Genomic Context 33
Contents • ix
2 3 DOWNSTREAM ANALYSIS - 34
231 Gene Annotation 34
232 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
261 Using Command Line Tools and R 36
262 Using the Chipster Software 37
263 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
1 AND PREPROCESSING 42
321 FastQC 42
322 PRINSEQ 43
323 Trimmomatic 44
3 3 READ QUALITY ISSUES 44
331 Base Quality 44
3311 Filtering 45
3312 Trimming 49
332 Ambiguous Bases 52
333 Adapters 54
334 Read Length 55
335 Sequence-Specific Bias and Mismatches Caused
by Random Hexamer Priming 56
336 GC Content 57
337 Duplicates 57
338 Sequence Contamination 59
339 Low-Complexity Sequences and PolyA Tails 59
x Q Contents
3 4 SUMMARY 60
REFERENCES 61
CHAPTER 4 n Aligning Reads to Reference 63
4 1 INTRODUCTION 63
4 2 ALIGNMENT PROGRAMS 64
421 Bowtie 64
422 TopHat 68
423 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
521 Transcriptome Assembly Is Different from
Genome Assembly 87
522 Complexity of Transcript Reconstruction 88
523 Assembly Process 89
524 de Bruijn Graph 90
525 Use of Abundance Information 91
5 3 DATA PREPROCESSING 92
531 Read Error Correction 93
532 Seecer 93
5 4 MAPPING-BASED ASSEMBLY 95
541 Cufflinks 95
542 Scripture 97
5 5 DE NOVO ASSEMBLY 98
551 Velvet + Oases 98
552 Trinity 100
Contents • 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
621 Tools for Annotation-Based Quality Control 111
6 3 QUANTITATION OF GENE EXPRESSION 116
631 Counting Reads per Genes 117
6311 HTSeq 117
632 Counting Reads per Transcripts 120
6321 Cufflinks 122
6322 eXpress 122
633 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
711 Installing R and Add-on Packages 132
712 Using R 133
7 2 OVERVIEW OF THE BIOCONDUCTOR PACKAGES 134
721 Software Packages 134
722 Annotation Packages 134
723 Experiment Packages 135
7 3 DESCRIPTIVE FEATURES OF THE
BIOCONDUCTOR PACKAGES 135
731 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 143
xii • 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
831 Biological Replication, Count Distributions, and
Choice of Software 150
8 4 NORMALIZATION 152
8 5 SOFTWARE USAGE EXAMPLES 154
851 Using Cuffdiff 154
852 Using Bioconductor Packages: DESeq, edgeR,
limma 158
853 Linear Models, the Design Matrix, and the
Contrast Matrix 158
8531 Design Matrix 159
8532 Contrast Matrix 160
854 Preparations Ahead of Differential Expression
Analysis 161
8541 Starting from BAM Files 162
8542 Starting from Individual Count Files 162
8543 Starting from an Existing Count Table 163
8544 Independent Filtering 163
855 Code Example for DESeq(2) 163
856 Visualization 164
857 For Reference: Code Examples for Other
Bioconductor Packages 168
858 Limma 169
859 SAMSeq (samr package) 170
8 5 10 edgeR 171
Contents • 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 216
xiv • 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) 254
Contents • 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
13 241 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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| title | RNA-seq data analysis a practical approach |
| title_auth | RNA-seq data analysis a practical approach |
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