Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to Next-Generation Biological Platforms
- 2 An Introduction to The Cancer Genome Atlas
- 3 DNA Variant Calling in Targeted Sequencing Data
- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data
- 5 Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data
- 7 Statistical Aspects of ChIP-Seq Analysis
- 8 Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
- 9 Multivariate Linear Models for GWAS
- 10 Bayesian Model Averaging for Genetic Association Studies
- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis
- 12 Methods for the Analysis of Copy Number Data in Cancer Research
- 13 Bayesian Models for Integrative Genomics
- 14 Bayesian Graphical Models for Integrating Multiplatform Genomics Data
- 15 Genetical Genomics Data: Some Statistical Problems and Solutions
- 16 A Bayesian Framework for Integrating Copy Number and Gene Expression Data
- 17 Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
- 18 Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
- 19 Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
- 20 Optimized Cross-Study Analysis of Microarray-Based Predictors
- 21 Functional Enrichment Testing: A Survey of Statistical Methods
- 22 Discover Trend and Progression Underlying High-Dimensional Data
- 23 Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
- Index
- Plate section
5 - Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
Published online by Cambridge University Press: 05 June 2013
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to Next-Generation Biological Platforms
- 2 An Introduction to The Cancer Genome Atlas
- 3 DNA Variant Calling in Targeted Sequencing Data
- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data
- 5 Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data
- 7 Statistical Aspects of ChIP-Seq Analysis
- 8 Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
- 9 Multivariate Linear Models for GWAS
- 10 Bayesian Model Averaging for Genetic Association Studies
- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis
- 12 Methods for the Analysis of Copy Number Data in Cancer Research
- 13 Bayesian Models for Integrative Genomics
- 14 Bayesian Graphical Models for Integrating Multiplatform Genomics Data
- 15 Genetical Genomics Data: Some Statistical Problems and Solutions
- 16 A Bayesian Framework for Integrating Copy Number and Gene Expression Data
- 17 Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
- 18 Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
- 19 Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
- 20 Optimized Cross-Study Analysis of Microarray-Based Predictors
- 21 Functional Enrichment Testing: A Survey of Statistical Methods
- 22 Discover Trend and Progression Underlying High-Dimensional Data
- 23 Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
- Index
- Plate section
Summary
Introduction
The rapid development of next-generation sequencing (NGS) technologies has revolutionized theway genomic research can be conducted. Among all successful applications of the NGS technologies, RNA-Seq has become an important tool for transcriptome profiling (Wang et al., 2009). The transcriptome is the complete set of transcripts in a cell under any given developmental stage or physiological condition. Comprehensively detecting, cataloging, and quantifying all of the components in the transcriptome are grand challenges in molecular biology and functional genomics. For the past 15 years, microarray (Schena et al., 1995; Lockhart et al., 1996) has been the technology of choice for studying transcriptome. Despite that much insight has been gained from microarray studies, factors such as the requirement of genomic sequence information when designing probes and substantial noise caused by cross-hybridization limited the application of microarray in more in-depth study of the transcriptome.
In RNA-Seq experiments, a population of RNA is converted to a library of cDNA fragments with adaptors attached to one end. Each molecule, after amplification, is then sequenced using one of the NGS technologies. After sequencing, the resulting reads are aligned to either the reference genome or known transcripts to produce a genome-scale transcriptional profile. (See Figure 5.1 for an illustration of the RNA-Seq experiment). Compared with microarray, RNA-Seq is able to provide more information about the transcriptome and possesses a list of advantages discussed next.
High resolution. The resolution of microarray expression measure is unable to go beyond the probe level. In contrast, the majority of reads generated from NGS instruments map to the reference genome with single-base resolution.
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- Advances in Statistical BioinformaticsModels and Integrative Inference for High-Throughput Data, pp. 105 - 125Publisher: Cambridge University PressPrint publication year: 2013
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