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Advances in statistical bioinformatics : models and integrative inference for high-throughput data

Author: Kim-Anh Do; Steven Qin; Marina Vannucci
Publisher: Cambridge : Cambridge University Press, 2013.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"Chapter 1 An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang, and Ganiraju Manyam The University of Texas. MD Anderson Cancer Center 1.1 Introduction When Sanger and Coulson first described a reliable, efficient method for DNA sequencing in 1975 (Sanger and Coulson, 1975), they made possible the full sequencing of both genes and entire genomes. Although the method was  Read more...
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Document Type: Book
All Authors / Contributors: Kim-Anh Do; Steven Qin; Marina Vannucci
ISBN: 9781107027527 1107027527
OCLC Number: 824608947
Description: xv, 481 pages : illustrations (some color) ; 24 cm
Contents: An introduction to next-generation biological platforms / Virginia Mohlere, Wenting Wang, and Ganiraju Manyam --
An introduction to the cancer genome atlas / Bradley M. Broom and Rehan Akbani --
DNA variant calling in targeted sequencing data / Wenyi Wang, Yu Fan, and Terence P. Speed --
Statistical analysis of mapped reads from mRNA-seq data / Ernest Turro and Alex Lewin --
Model-based methods for transcript expression-level quantification in RNA-seq / Zhaonan Sun --
Bayesian model-based approaches for solexa sequencing data / Riten Mitra, Peter Mueller, and Yuan Ji --
Statistical aspects of ChIP-seq analysis / Jonathan Cairns, Andy G. Lynch, and Simon Tavaré --
Bayesian modeling of ChIP-seq data from transcription factor to nucleosome positioning / Raphael Gottardo and Sangsoon Woo --
Multivariate Linear models for GWAS / Chiara Sabatti --
Bayesian Model averaging for genetic association studies / Christine Peterson [and others] --
Whole-genome multi-SNP-phenotype association analysis / Yongtao Guan and Kai Wang --
Methods for the analysis of copy number data in cancer research / Bradley M. Broom [and others] --
Bayesian models for integrative genomics / Francesco C. Stingo and Marina Vannucci --
Bayesian graphical models for integrating multiplatform genomics data / Wenting Wang [and others] --
Genetical genomics data: some statistical problems and solutions / Hongzhe Li --
A Bayesian framework for integrating copy number and gene expression data / Yuan Ji, Filippo Trentini, and Peter Mueller --
Application of Bayesian sparse factor analysis models in bioinformatics / Haisu Ma and Hongyu Zhao --
Predicting cancer subtypes using survival-supervised latent Dirichlet allocation models / Keegan Korthauer, John Dawson, and Christina Kendziorski --
Regularization techniques for highly correlated gene expression data with unknown group structure / Brent A. Johnson --
Optimized cross-study analysis of microarray-based predictors / Xiaogang Zhong [and others] --
Functional enrichment testing: a survey of statistical methods / Laila M. Poisson and Debashis Ghosh --
Discover trend and progression underlying high-dimensional data / Peng Qiu --
Bayesian phylogenetics adapts to comprehensive infectious disease sequence data / Jennifer A. Tom, Janet S. Sinsheimer, and Marc A. Suchard.
Responsibility: edited by Kim-Anh Do, the University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX.

Abstract:

This book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations.  Read more...

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    schema:description ""Chapter 1 An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang, and Ganiraju Manyam The University of Texas. MD Anderson Cancer Center 1.1 Introduction When Sanger and Coulson first described a reliable, efficient method for DNA sequencing in 1975 (Sanger and Coulson, 1975), they made possible the full sequencing of both genes and entire genomes. Although the method was resource-intensive, many institutions invested in the necessary equipment, and Sanger sequencing remained the standard for the next 30 years. Refinement of the process increased read lengths from around 25 to 2 Mohlere, Wang, and Manyam almost 750 base pairs (Schadt et al., 2010, fig. 1). While this greatly increased efficiency and reliability, the Sanger method still required not only large equipment but significant human investment, as the process requires the work of several people. This prompted researchers and companies such as Applied Biosystems to seek improved sequencing techniques and instruments. Starting in the late 2000s, new instruments came on the market that, although they actually decreased read length, lessened run time and could be operated more easily with fewer human resources (Schadt et al., 2010). Despite discoveries that have illuminated new therapeutic targets, clarified the role of specific mutations in clinical response, and yielded new methods for diagnosis and predicting prognosis (Chin et al., 2011), the initial promise of genomic data has largely remained so far unfulfilled. The difficulties are numerous"--"@en ;
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