Preface |
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xvii | |
Acknowledgments |
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xxi | |
Authors |
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xxiii | |
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Chapter 1 Introduction to RNA-seq |
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1 | (26) |
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1 | (2) |
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3 | (1) |
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1.3 Quality Control Of RNA |
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4 | (2) |
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6 | (3) |
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1.5 Major RNA-Seq Platforms |
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9 | (5) |
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9 | (1) |
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10 | (1) |
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11 | (1) |
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11 | (1) |
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1.5.5 Pacific Biosciences |
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12 | (1) |
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1.5.6 Nanopore Technologies |
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13 | (1) |
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14 | (7) |
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1.6.1 Protein Coding Gene Structure |
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14 | (2) |
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1.6.2 Novel Protein-Coding Genes |
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16 | (1) |
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1.6.3 Quantifying and Comparing Gene Expression |
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16 | (1) |
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1.6.4 Expression Quantitative Train Loci (eQTL) |
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17 | (1) |
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1.6.5 Single-Cell RNA-seq |
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18 | (1) |
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18 | (1) |
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19 | (1) |
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1.6.8 Long Noncoding RNAs |
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19 | (1) |
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1.6.9 Small Noncoding RNAs (miRNA-seq) |
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20 | (1) |
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1.6.10 Amplification Product Sequencing (Ampli-seq) |
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20 | (1) |
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1.7 Choosing An RNA-Seq Platform |
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21 | (6) |
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1.7.1 Eight General Principles for Choosing an RNA-seq Platform and Mode of Sequencing |
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21 | (1) |
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1.7.1.1 Accuracy: How Accurate Must the Sequencing Be? |
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21 | (1) |
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1.7.1.2 Reads: How Many Do I Need? |
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22 | (1) |
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1.7.1.3 Length: How Long Must the Reads Be? |
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23 | (1) |
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1.7.1.4 SR or PE: Single Read or Paired End? |
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23 | (1) |
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1.7.1.5 RNA or DNA: Am I Sequencing RNA or DNA? |
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23 | (1) |
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1.7.1.6 Material: How Much Sample Material Do I Have? |
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24 | (1) |
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1.7.1.7 Costs: How Much Can I Spend? |
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24 | (1) |
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1.7.1.8 Time: When Does the Work Need to Be Completed? |
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24 | (1) |
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25 | (1) |
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25 | (2) |
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Chapter 2 Introduction to RNA-seq Data Analysis |
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27 | (14) |
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27 | (3) |
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2.2 Differential Expression Analysis Workflow |
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30 | (4) |
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2.2.1 Step 1: Quality Control of Reads |
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31 | (1) |
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2.2.2 Step 2: Preprocessing of Reads |
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31 | (1) |
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2.2.3 Step 3: Aligning Reads to a Reference Genome |
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31 | (1) |
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2.2.4 Step 4: Genome-Guided Transcriptome Assembly |
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32 | (1) |
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2.2.5 Step 5: Calculating Expression Levels |
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32 | (1) |
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2.2.6 Step 6: Comparing Gene Expression between Conditions |
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33 | (1) |
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2.2.7 Step 7: Visualization of Data in Genomic Context |
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33 | (1) |
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34 | (1) |
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34 | (1) |
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2.3.2 Gene Set Enrichment Analysis |
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34 | (1) |
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2.4 Automated Workflows And Pipelines |
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35 | (1) |
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2.5 Hardware Requirements |
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35 | (1) |
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2.6 Following The Examples In The Book |
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36 | (4) |
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2.6.1 Using Command Line Tools and R |
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36 | (1) |
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2.6.2 Using the Chipster Software |
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37 | (2) |
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39 | (1) |
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40 | (1) |
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40 | (1) |
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Chapter 3 Quality Control and Preprocessing |
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41 | (22) |
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41 | (1) |
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3.2 Software For Quality Control And Preprocessing |
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42 | (2) |
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42 | (1) |
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43 | (1) |
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44 | (1) |
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44 | (16) |
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44 | (1) |
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45 | (4) |
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49 | (3) |
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52 | (2) |
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54 | (1) |
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55 | (1) |
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3.3.5 Sequence-Specific Bias and Mismatches Caused by Random Hexamer Priming |
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56 | (1) |
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57 | (1) |
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57 | (2) |
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3.3.8 Sequence Contamination |
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59 | (1) |
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3.3.9 Low-Complexity Sequences and PolyA Tails |
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59 | (1) |
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60 | (3) |
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61 | (2) |
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Chapter 4 Aligning Reads to Reference |
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63 | (22) |
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63 | (1) |
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64 | (13) |
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64 | (4) |
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68 | (5) |
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73 | (4) |
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4.3 Alignment Statistics And Utilities For Manipulating Alignment Files |
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77 | (4) |
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4.4 Visualizing Reads In Genomic Context |
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81 | (1) |
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82 | (3) |
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83 | (2) |
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Chapter 5 Transcriptome Assembly |
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85 | (24) |
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85 | (2) |
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87 | (5) |
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5.2.1 Transcriptome Assembly Is Different From Genome Assembly |
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87 | (1) |
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5.2.2 Complexity of Transcript Reconstruction |
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88 | (1) |
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89 | (1) |
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90 | (1) |
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5.2.5 Use of Abundance Information |
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91 | (1) |
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92 | (3) |
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5.3.1 Read Error Correction |
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93 | (1) |
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93 | (2) |
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5.4 Mapping-Based Assembly |
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95 | (3) |
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95 | (2) |
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97 | (1) |
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98 | (6) |
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98 | (2) |
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100 | (4) |
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104 | (5) |
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106 | (3) |
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Chapter 6 Quantitation and Annotation-Based Quality Control |
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109 | (22) |
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109 | (1) |
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6.2 Annotation-Based Quality Metrics |
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110 | (6) |
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6.2.1 Tools For Annotation-Based Quality Control |
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111 | (5) |
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6.3 Quantitation Of Gene Expression |
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116 | (12) |
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6.3.1 Counting Reads per Genes |
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117 | (1) |
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117 | (3) |
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6.3.2 Counting Reads per Transcripts |
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120 | (2) |
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122 | (1) |
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122 | (4) |
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6.3.3 Counting Reads per Exons |
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126 | (2) |
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128 | (3) |
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129 | (2) |
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Chapter 7 RNA-seq Analysis Framework in R and Bioconductor |
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131 | (16) |
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131 | (3) |
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7.1.1 Installing R and Add-on Packages |
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132 | (1) |
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133 | (1) |
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7.2 Overview Of The Bioconductor Packages |
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134 | (1) |
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134 | (1) |
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7.2.2 Annotation Packages |
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134 | (1) |
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7.2.3 Experiment Packages |
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135 | (1) |
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7.3 Descriptive Features Of The Bioconductor Packages |
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135 | (3) |
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135 | (3) |
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7.4 Representing Genes And Transcripts In R |
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138 | (3) |
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7.5 Representing Genomes In R |
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141 | (2) |
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7.6 Representing SNPs In R |
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143 | (1) |
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7.7 Forging New Annotation Packages |
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143 | (3) |
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146 | (1) |
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146 | (1) |
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Chapter 8 Differential Expression Analysis |
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147 | (34) |
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147 | (1) |
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8.2 Technical Vs. Biological Replicates |
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148 | (1) |
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8.3 Statistical Distributions In RNA-Seq Data |
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149 | (3) |
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8.3.1 Biological Replication, Count Distributions, and Choice of Software |
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150 | (2) |
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152 | (2) |
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8.5 Software Usage Examples |
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154 | (22) |
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154 | (4) |
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8.5.2 Using Bioconductor Packages: DESeq, edgeR, limma |
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158 | (1) |
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8.5.3 Linear Models, the Design Matrix, and the Contrast Matrix |
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158 | (1) |
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159 | (1) |
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160 | (1) |
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8.5.4 Preparations Ahead of Differential Expression Analysis |
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161 | (1) |
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8.5.4.1 Starting from BAM Files |
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162 | (1) |
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8.5.4.2 Starting from Individual Count Files |
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162 | (1) |
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8.5.4.3 Starting from an Existing Count Table |
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163 | (1) |
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8.5.4.4 Independent Filtering |
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163 | (1) |
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8.5.5 Code Example for DESeq(2) |
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163 | (1) |
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164 | (4) |
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8.5.7 For Reference: Code Examples for Other Bioconductor Packages |
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168 | (1) |
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169 | (1) |
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8.5.9 SAMSeq (samr package) |
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170 | (1) |
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171 | (1) |
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8.5.11 DESeq2 Code Example for a Multifactorial Experiment |
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171 | (3) |
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8.5.12 For Reference: edgeR Code Example |
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174 | (1) |
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8.5.13 Limma Code Example |
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175 | (1) |
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176 | (5) |
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177 | (4) |
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Chapter 9 Analysis of Differential Exon Usage |
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181 | (18) |
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181 | (2) |
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9.2 Preparing The Input Files For Dexseq |
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183 | (1) |
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184 | (1) |
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9.4 Accessing The ExonCountSet Object |
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185 | (2) |
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9.5 Normalization And Estimation Of The Variance |
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187 | (3) |
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9.6 Test For Differential Exon Usage |
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190 | (3) |
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193 | (5) |
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198 | (1) |
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198 | (1) |
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Chapter 10 Annotating the Results |
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199 | (18) |
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199 | (1) |
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10.2 Retrieving Additional Annotations |
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200 | (8) |
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10.2.1 Using an Organism-Specific Annotation Package to Retrieve Annotations for Genes |
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201 | (4) |
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10.2.2 Using BioMart to Retrieve Annotations for Genes |
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205 | (3) |
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10.3 Using Annotations For Ontological Analysis Of Gene Sets |
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208 | (2) |
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10.4 Gene Set Analysis In More Detail |
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210 | (6) |
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10.4.1 Competitive Method Using GOstats Package |
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211 | (2) |
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10.4.2 Self-Contained Method Using Globaltest Package |
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213 | (2) |
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10.4.3 Length Bias Corrected Method |
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215 | (1) |
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216 | (1) |
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216 | (1) |
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217 | (20) |
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217 | (2) |
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218 | (1) |
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218 | (1) |
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219 | (1) |
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219 | (13) |
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220 | (4) |
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224 | (2) |
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226 | (2) |
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228 | (2) |
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11.2.5 Visualizing Gene and Transcript Structures |
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230 | (2) |
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11.3 Finalizing The Plots |
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232 | (2) |
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234 | (3) |
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235 | (2) |
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Chapter 12 Small Noncoding RNAs |
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237 | (22) |
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237 | (2) |
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239 | (4) |
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12.3 Microrna Off-Set RNAS (moRNAs) |
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243 | (1) |
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12.4 Piwi-Associated RNAS (piRNAs) |
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243 | (1) |
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12.5 Endogenous Silencing RNAs (endo-siRNAs) |
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244 | (1) |
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12.6 Exogenous Silencing RNAs (exo-siRNAs) |
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244 | (1) |
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12.7 Transfer RNAs (tRNAs) |
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245 | (1) |
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12.8 Small Nucleolar RNAs (snoRNAs) |
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245 | (1) |
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12.9 Small Nuclear RNAs (snRNAs) |
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245 | (1) |
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12.10 Enhancer-Derived RNAs (eRNA) |
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246 | (1) |
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12.11 Other Small Noncoding RNAs |
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246 | (2) |
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12.12 Sequencing Methods For Discovery Of Small Noncoding RNAs |
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248 | (7) |
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248 | (3) |
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251 | (3) |
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254 | (1) |
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12.12.4 Global Run-On Sequencing (GRO-seq) |
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254 | (1) |
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255 | (4) |
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255 | (4) |
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Chapter 13 Computational Analysis of Small Noncoding RNA Sequencing Data |
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259 | (28) |
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259 | (1) |
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13.2 Discovery Of Small RNAs---miRDeep2 |
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260 | (8) |
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260 | (3) |
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13.2.2 FASTA Files of Known miRNAs |
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263 | (1) |
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13.2.3 Setting up the Run Environment |
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263 | (3) |
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266 | (1) |
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266 | (2) |
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268 | (3) |
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13.3.1 Running miRanalyzer |
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271 | (1) |
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13.4 miRNA Target Analysis |
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271 | (5) |
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13.4.1 Computational Prediction Methods |
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272 | (2) |
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13.4.2 Artificial Intelligence Methods |
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274 | (1) |
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13.4.3 Experimental Support-Based Methods |
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275 | (1) |
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13.5 miRNA-Seq And mRNA-Seq Data Integration |
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276 | (1) |
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13.6 Small RNA Databases And Resources |
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277 | (7) |
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13.6.1 RNA-seq Reads of miRNAs in miRBase |
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277 | (2) |
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13.6.2 Expression Atlas of miRNAs |
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279 | (2) |
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13.6.3 Database for CLIP-seq and Degradome-seq Data |
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281 | (1) |
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13.6.4 Databases for miRNAs and Disease |
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281 | (1) |
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13.6.5 General Databases for the Research Community and Resources |
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282 | (1) |
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282 | (2) |
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284 | (3) |
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284 | (3) |
Index |
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287 | |