Statistical Analysis of Next Generation Sequencing Data
Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from bas…
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- Full title :Statistical Analysis of Next Generation Sequencing Data.
Summary
Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number.
Read More ↓Edition Statements
- Publication Date:2014.
Publication Statements
- Publisher(s):Springer
- Place of Publication:Dordrecht
- Language:eng
- Publication Date:2014.
Notes
5.3.1 Examination of the Tradeoff Between Depthand Replication.
The Physical Object
- Description:1 online resource (438 pages)
Edition Identifiers
- ISBN:9783319072128, 3319072129
- oclc:ocn894170109
- LC Classification:QH323.5
- Dewey Decimal:572.8633015195
Table of Contents
Preface; Acknowledgements; About the Editors; Contents; 1 Statistical Analyses of Next Generation Sequencing Data: An Overview; 1.1 Introduction; 1.1.1 DNA: The Unit of Sequencing; 1.1.2 Sequencing Technologies: An Overview; 1.1.3 Downstream Applications; 1.2 Examples of NGS Platforms; 1.2.1 SOLiD by Applied Biosystem; 1.2.2 Illumina/Solexa Genome Analyzer; 1.2.3 Ion Semiconductor Sequencing; 1.2.4 Single Molecule Real-Time Sequencing; 1.3 Statistical Tools for Using Sequence Reads; 1.3.1 Data Quality and Reproducibility; 1.3.2 Basecalling; 1.3.3 Alignment and Assembly Tools.
1.3.4 Post-Alignment Analysis1.3.5 R and Bioconductor Packages; 1.4 Conclusion; References; 2 Using RNA-seq Data to Detect Differentially Expressed Genes; 2.1 Introduction: RNA-seq Data; 2.2 Statistical Methods for Testing Differential Expression; 2.2.1 Simple Approaches; 2.2.2 Tests Based on Extensions of the Poisson Distribution; 2.2.3 Negative Binomial and Quasi-Likelihood Tests; 2.2.4 Other Methods; 2.2.5 Bayesian and Empirical Bayes Approaches; 2.3 Software for Differential Expression in RNA-seq Data; 2.4 Comparison of Methods for Detecting Differential Expression; 2.5 Discussion.
3.4 Case Study: Trans…
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