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
6 - Bayesian Model-Based Approaches for Solexa Sequencing Data
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
Recent advances in next-generation sequencing have hugely impacted biological research through high-throughput platforms that generate megabases of sequence data per day. These technologies improve both speed and cost and have found applications in genotyping, protein-DNA interactions (Barski et al., 2007; Mikkelsen et al., 2007), transcriptome analysis (Friedländer et al., 2008; Hafner et al., 2008; Vera et al., 2008), and de novo genome assembly (Chaisson and Pevzner, 2008). In this chapter, we focus on the Illumina/Solexa sequencing platform. However, data from other technologies have similar characteristics, and we expect models similar to the one presented here to remain useful also for these technologies.
Solexa sequencing (www.illumina.com) produces millions of polymerase chain reaction (PCR) amplified and labeled sequences of short reads. For each short read, the measurements of their fluorescent intensities are stored in an I × 4 matrix, where I is the length of the read (e.g., I = 36). Such amatrix corresponds to a colony. The positions i = 1, …, I in the short read are sequenced in cycles by a biochemical procedure called sequencing-by-synthesis. As a result, each row of the colony matrix contains measurements from a cycle in the experiment in which the sequence of a single base is synthesized. At each cycle, all four nucleotides (A, C, G, and T) labeled with four different fluorescent dyes are probed, thus producing a quadruple vector of fluorescent intensity scores.
- Type
- Chapter
- Information
- Advances in Statistical BioinformaticsModels and Integrative Inference for High-Throughput Data, pp. 126 - 137Publisher: Cambridge University PressPrint publication year: 2013