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Statistical analysis of next generation sequencing data / [edited by] Somnath Datta and Dan Nettleton.

Contributor(s): Material type: TextTextSeries: Frontiers in probability and the statistical sciencesPublication details: New York : Springer, 2014.Description: xiv, 432 p. : illustrations (mostly color) ; 25 cmISBN:
  • 9783319072111 (hard cover : alk. paper)
Subject(s): DDC classification:
  • 23 D234 000SB:570
Contents:
1. Statistical analyses of next generation sequencing data : an overview -- 2. Using RNA-seq data to detect differentially expressed genes -- 3. Differential expression analysis of complex RNA-seq experiments using edgeR -- 4. Analysis of next generation sequencing data using integrated nested Laplace approximation (INLA) -- 5. Design of RNA sequencing experiments -- 6. Measurement, summary, and methodological variation in RNA-sequencing -- 7. DE-FPCA: Testing gene differential expression and exon usage through functional principal component analysis-- 8. Mapping of expression quantitative trait Loci using RNA-seq data -- 9. The role of spike-in standards in the normalization of RNA-seq -- 10. Cluster analysis of RNA-sequencing data -- 11. Classification of RNA-seq data -- 12. Isoform expression analysis based on RNA-seq data -- 13. RNA isoform discovery through goodness of fit diagnostics -- 14. MOSAiCS-HMM : a model-based approach for detecting regions of histone modifications from ChIP-seq data -- 15. Hierarchical Bayesian models for ChIP-seq data -- 16. Genotype calling and haplotype phasing from next generation sequencing data -- 17. Analysis of metagenomic data -- 18. Detecting copy number changes and structural rearrangements using DNA sequencing -- 19. Statistical methods for the analysis of next generation sequence data from paired tumor-normal samples -- 20. Statistical considerations in the analysis of rare variants-- Index.
Summary: 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 variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine.
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Includes index.

1. Statistical analyses of next generation sequencing data : an overview --
2. Using RNA-seq data to detect differentially expressed genes --
3. Differential expression analysis of complex RNA-seq experiments using edgeR --
4. Analysis of next generation sequencing data using integrated nested Laplace approximation (INLA) --
5. Design of RNA sequencing experiments --
6. Measurement, summary, and methodological variation in RNA-sequencing --
7. DE-FPCA: Testing gene differential expression and exon usage through functional principal component analysis--
8. Mapping of expression quantitative trait Loci using RNA-seq data --
9. The role of spike-in standards in the normalization of RNA-seq --
10. Cluster analysis of RNA-sequencing data --
11. Classification of RNA-seq data --
12. Isoform expression analysis based on RNA-seq data --
13. RNA isoform discovery through goodness of fit diagnostics --
14. MOSAiCS-HMM : a model-based approach for detecting regions of histone modifications from ChIP-seq data --
15. Hierarchical Bayesian models for ChIP-seq data --
16. Genotype calling and haplotype phasing from next generation sequencing data --
17. Analysis of metagenomic data --
18. Detecting copy number changes and structural rearrangements using DNA sequencing --
19. Statistical methods for the analysis of next generation sequence data from paired tumor-normal samples --
20. Statistical considerations in the analysis of rare variants--
Index.

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 variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine.

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