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作者 Do, Kim-Anh
書名 Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data
出版項 New York : Cambridge University Press, 2013
©2013
國際標準書號 9781107248588 (electronic bk.)
9781107027527
book jacket
說明 1 online resource (516 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
附註 Intro -- Contents -- List of Contributors -- Preface -- 1 An Introduction to Next-Generation Biological Platforms -- Virginia Mohlere, Wenting Wang, and Ganiraju Manyam -- 1.1 Introduction -- 1.2 The Biology of Gene Silencing -- 1.2.1 DNA Methylation -- 1.2.2 RNA Interference -- 1.3 High-Throughput Profiling -- 1.3.1 Molecular Inversion Probe Arrays -- 1.3.2 Array Comparative Genomic Hybridization (aCGH) -- 1.3.3 Genome-Wide Association Studies -- 1.3.4 Reverse-Phase Protein Array -- 1.4 Next-Generation Sequencing -- 1.4.1 Whole-Genome and Whole-Exome Sequencing -- 1.4.2 ChIP-Seq -- 1.4.3 RNA-Seq -- 1.4.4 BS-seq -- 1.5 NGS Data Management and Analysis -- 1.6 Platform Integration -- Acknowledgments -- References -- References -- 2 An Introduction to The Cancer Genome Atlas -- Bradley M. Broom and Rehan Akbani -- 2.1 Introduction -- 2.2 History and Goals of the TCGA Project -- 2.3 Sample Collection and Processing -- 2.3.1 Step 1: Tissue Collection -- 2.3.2 Step 2: Quality Control and DNA/RNA Extraction -- 2.3.3 Step 3: Molecular Profiling and Sequencing -- 2.3.4 Step 4: Data Collection and Public Distribution -- 2.3.5 Step 5: Data Analysis -- 2.4 Data Processing, Storage, and Access -- 2.4.1 TCGA Barcodes and UUIDs -- 2.4.2 The Data Coordinating Center -- 2.4.3 Data Access Matrix -- 2.4.4 Bulk Download -- 2.4.5 HTTP -- 2.4.6 CGHub -- 2.4.7 Sample and Data Relationship Format (SDRF) and Investigation Description Format (IDF) Files -- 2.4.8 File Format -- 2.4.9 Version -- 2.5 Tools for Visualizing and Analyzing TCGA Data -- 2.5.1 cBio Cancer Genomics Portal -- 2.5.2 MBatch Portal -- 2.5.3 Next-Generation Clustered Heat Maps -- 2.5.4 Regulome Explorer -- 2.5.5 Integrative Genome Viewer -- 2.5.6 Cancer Genomics Browser -- 2.6 Summary -- Acknowledgments -- References -- References -- 3 DNA Variant Calling in Targeted Sequencing Data
Wenyi Wang, Yu Fan, and Terence P. Speed -- 3.1 Introduction -- 3.2 Background -- 3.2.1 Single-Nucleotide Variation -- 3.2.2 Long Padlock Probes -- 3.2.3 Array-Based Resequencing -- 3.3 Sequence Robust Multiarray Analysis -- 3.3.1 Quality Control -- 3.3.2 Variant Calling -- 3.4 Application of SRMA -- 3.4.1 Candidate Gene Study for Mitochondrial Diseases -- 3.4.2 Validation Results -- 3.4.3 Biological Findings -- 3.5 Conclusion -- Appendix -- References -- References -- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data -- Ernest Turro and Alex Lewin -- 4.1 Background -- 4.1.1 RNA Biology -- 4.1.2 RNA Technology -- 4.2 Mapping and Assembly Strategies -- 4.2.1 De Novo Assembly of the Transcriptome -- 4.2.2 Genome-Guided Assembly of the Transcriptome -- 4.2.3 Alignment to a Reference Transcriptome -- 4.3 Modeling Expression Levels -- 4.3.1 Poisson Model for Expression Quantification -- 4.4 Normalization -- 4.4.1 RPKM Normalization -- 4.4.2 Other Scaling Normalizations -- 4.4.3 Adjusted Transcript Lengths -- 4.4.4 Sequencing Bias -- 4.4.5 Fragment Size Distribution -- 4.4.6 Quantile Normalization -- 4.4.7 Nonparametric normalization factors -- 4.5 Modeling Overdispersion -- 4.6 Beyond Poisson and Negative Binomial Families -- 4.7 Differential Expression Analysis -- 4.7.1 Frequentist Methods -- 4.7.2 Bayesian Methods -- 4.7.3 Nonparametric Approaches -- 4.8 Allelic Imbalance -- 4.9 Concluding Remarks -- References -- References -- 5 Model-Based Methods for Transcript Expression-Level Quantification -- Zhaonan Sun, Han Wu, Zhaohui Qin, and Yu Zhu -- 5.1 Introduction -- 5.2 Bias and Variation in RNA-Seq Experiments -- 5.2.1 Experimental Sources -- 5.2.2 Biological Sources -- 5.2.3 Other Sources -- 5.3 Base-Level Reads Count Data -- 5.4 Quantification Methods -- 5.4.1 Generalized Poisson model and GPseq -- 5.4.2 Poisson Mixed Effects Model and POME
5.4.3 Poisson Mixture Model and PMseq -- 5.4.4 Poisson Regression with Sequencing Preference Correction (mseq) -- 5.4.5 Cufflinks with Bias Adjustment -- 5.4.6 RPKM with GC-Content Correction -- 5.5 Comparison Results -- 5.6 Discussions -- References -- References -- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data -- Riten Mitra, Peter Mueller, and Yuan Ji -- 6.1 Introduction -- 6.2 A Hierarchical Model for GA-I -- 6.3 Analysis and Results for GA-I -- 6.4 Application to GA-II -- 6.5 Discussion -- Acknowledgment -- References -- References -- 7 Statistical Aspects of ChIP-Seq Analysis -- Jonathan Cairns, Andy G. Lynch, and Simon Tavare -- 7.1 Introduction: The Purpose of the ChIP-seq Experiment -- 7.2 Aims -- 7.3 Experimental Overview -- 7.3.1 Control Experiments -- 7.3.2 Paired-End Sequencing -- 7.3.3 The Data -- 7.3.4 Potential Sources of Error and Bias -- 7.3.5 Histones/Nucleosomes -- 7.3.6 ReChIP -- 7.3.7 Other Experiments -- 7.3.8 Estimating Fragment Length -- 7.4 Peak-Calling in TF Data -- 7.4.1 Strategy-Independent Issues -- 7.4.2 Count-Based Strategies -- 7.4.3 Shape-Based Strategies -- 7.5 Peak-calling in Histone Mark Data -- 7.6 Validation -- 7.6.1 Functional Binding Site Validation -- 7.6.2 Binding Site Validation -- 7.6.3 Motif Analysis -- 7.6.4 Replication -- 7.6.5 Technical and Biological Replication -- 7.7 Assessing the Reliability of Peak-Callers -- 7.8 Differential Count-Based Strategies -- 7.8.1 Analysis Protocol -- 7.9 The Future of ChIP-seq -- 7.9.1 Integrating ChIP-seq with Expression Data -- References -- References -- Raphael Gottardo and Sangsoon Woo -- 8 Bayesian Modeling of ChIP-Seq Data -- 8.1 Introduction -- 8.2 ChIP-seq Analysis -- 8.2.1 The PICS Framework -- 8.2.2 Other Methods to be Compared -- 8.2.3 Application to the FOXA1 Data -- 8.3 Nucleosome Positioning -- 8.3.1 The PING Framework
8.3.2 Methods to be Compared -- 8.3.3 Application to Experimental Data -- 8.4 Bioconductor Pipeline -- 8.5 Discussion -- 8.6 Acknowledgments -- References -- References -- 9 Multivariate Linear Models for GWAS -- Chiara Sabatti -- 9.1 Introduction -- 9.2 The Polygenic Model -- 9.3 Analysis of GWAS -- 9.4 Challenges in Multivariate Linear Models for GWAS -- 9.5 Lasso Approaches to GWAS -- 9.6 Bayesian Approaches to GWAS -- 9.7 Conclusion -- References -- References -- 10 Bayesian Model Averaging for Genetic Association Studies -- Christine Peterson, Michael Swartz,Sanjay Shete, and Marina Vannucci -- 10.1 Genetic Association Studies -- 10.2 Statistical Analysis for Association Studies -- 10.2.1 Bayesian Variable Selection -- 10.3 Stochastic Search Variable Selection -- 10.3.1 Prior Specification -- 10.3.2 MCMC Sampling -- 10.3.3 Posterior Inference -- 10.3.4 Decision Rules and FDR Control -- 10.3.5 SSVS for Genetic Association Studies -- 10.4 Application: Folate Metabolism and Lung Cancer -- 10.5 Discussion -- References -- References -- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis -- Yongtao Guan and Kai Wang -- 11.1 Introduction -- 11.1.1 Single-SNP Analysis has Difficulties in AssessingOverall Association Signals -- 11.1.2 Whole-Genome Multi-SNP Analysis -- A Nontechnical Summary -- 11.2 Bayesian Variable Selection Regression -- 11.2.1 Prior Relating 2a, with Heritability -- 11.2.2 Computation and Inference -- 11.3 Penalized Regression -- 11.4 Estimate PVE Without Identifying Causal Variants -- 11.5 Binary Phenotypes -- 11.5.1 Extension of BVSR to Binary Phenotype -- 11.5.2 Machine Learning Approach -- 11.6 Discussion -- References -- References -- 12 Methods for the Analysis of Copy Number Data in Cancer Research -- Bradley M. Broom, Kim-Anh Do, Melissa Bondy, Patricia Thompson, and Kevin Coombes -- 12.1 Introduction
12.2 Allele Ratio and the Balance Statistic -- 12.3 Modeling Tumor Ploidy and DNA Purity -- 12.4 Further Examples -- 12.5 Estimating Copy Number -- 12.5.1 Early Methods -- 12.5.2 OncoSNP -- 12.5.3 ASCAT -- 12.5.4 PICNIC -- 12.5.5 FREEC -- 12.5.6 TAPS -- 12.5.7 Parent-Specific Copy Number -- 12.5.8 CNAnorm -- 12.5.9 Tightrope -- 12.5.10 ABSOLUTE -- 12.6 Summary of Tumor DNA Purity and Ploidy Results -- 12.7 Summary -- Acknowledgments -- References -- References -- 13 Bayesian Models for Integrative Genomics -- Francesco C. Stingo and Marina Vannucci -- 13.1 Introduction -- 13.2 Models That Integrate External InformationWith Experimental Data -- 13.2.1 Linear Models for Pathway and Gene Selection -- 13.2.2 Biomarker Selection in Mixture Models -- 13.3 Models That Integrate Data From Different Platforms -- 13.3.1 Graphical Models to Infer Regulatory Networks -- 13.4 Conclusion -- Acknowledgments -- References -- References -- 14 Bayesian Graphical Models for Integrating Multiplatform Data -- Wenting Wang, Veerabhadran Baladandayuthapani, Chris C. Holmes, and Kim-Anh Do -- 14.1 Introduction -- 14.2 Graph-Based Integration of Multiplatform Data -- 14.3 Objective Bayesian Model Selection for GGM -- 14.4 Application Data Example -- 14.4.1 Clinical Characteristics -- 14.4.2 microRNA Data Set -- 14.4.3 mRNA Data Set -- 14.4.4 Analysis Results -- 14.5 Discussion -- Acknowledgment -- References -- References -- 15 Genetical Genomics Data: Some Statistical Problems and Solutions -- HONGZHE LI -- 15.1 Introduction and Review of Current Methods -- 15.1.1 Expression Quantitative Trait Loci (eQTL) -- 15.1.2 Methods for Identifying Cis- and Trans-eQTLs -- 15.2 Differential Co-expression Analysis -- 15.2.1 Dynamic Co-expression Analysis -- 15.2.2 Gene-set Based Differential Co-expression Analysis -- 15.3 Conditional Gaussian Graphical Model
15.3.1 Estimation Based on 1 Penalization
This book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
鏈接 Print version: Do, Kim-Anh Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data New York : Cambridge University Press,c2013 9781107027527
主題 Bioinformatics -- Statistical methods.;Biometry.;Genetics -- Technique
Electronic books
Alt Author Qin, Zhaohui Steve
Vannucci, Marina
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