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Statistical Genomics: Methods and Protocols



Statistical Genomics: Methods and Protocols

Author: Ewy Mathé and Sean Davis

Publisher: Humana Press

Genres:

Publish Date: March 24, 2016

ISBN-10: 1493935763

Pages: 418

File Type: PDF

Language: English

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Book Preface

Statistical Genomics: Methods and Protocols

Statistical Analysis of Genomic Data is, indeed, a very broad topic. We have attempted in this volume to provide chapters with cross-cutting groundwork materials, public data repositories, common applications of statistical analysis in genomics, and some representative toolsets for operating on genomic data. While we cannot be comprehensive in a single volume, we have tried to provide a breadth of both applications and tools. The authors of the individual chapters have largely focused on practical aspects of their topics, as we feel that application is an integral part of learning about statistical analysis of genomic data.

More specifically, the volume is divided into four parts. In the first part, we have included overview material and resources that can be applied across topics later in the book. In the second part, a couple of prominent public repositories for genomic data are covered in some depth. In the third part, several different biological applications of statistical genomics are presented. In the fourth and last part, software tools that can be used to facilitate ad hoc analysis and data integration are highlighted. Finally, we thank the chapter authors for the generosity of their time and insight in preparing their excellent contributions.

Columbus, OH, USA Ewy Mathe´
Bethesda, MD, USA Sean Davis

 

Contents

PART1 GROUNDWORK
1 Overview of Sequence Data Formats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Hongen Zhang
2 Integrative Exploratory Analysis of Two or More Genomic Datasets . . . . . . . . . 19
Chen Meng and Aedin Culhane
3 Study Design for Sequencing Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Loren A. Honaas, Naomi S. Altman, and Martin Krzywinski
4 Genomic Annotation Resources in R/Bioconductor . . . . . . . . . . . . . . . . . . . . . . . 67
Marc R.J. Carlson, Herve´ Page`s, Sonali Arora, Valerie Obenchain, and Martin Morgan
PART II PUBLIC GENOMIC DATA
5 The Gene Expression Omnibus Database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Emily Clough and Tanya Barrett
6 A Practical Guide to The Cancer Genome Atlas (TCGA). . . . . . . . . . . . . . . . . . . 111
Zhining Wang, Mark A. Jensen, and Jean Claude Zenklusen
PART III APPLICATIONS
7 Working with Oligonucleotide Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Benilton S. Carvalho
8 Meta-Analysis in Gene Expression Studies . . .. . . . . . . . . . . . . . . . . . . . . 161
Levi Waldron and Markus Riester
9 Practical Analysis of Genome Contact Interaction Experiments . . . . . . . . . . . . . 177
Mark A. Carty and Olivier Elemento
10 Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data . . . . . . . . . .  . . . 191
Matthias Lienhard and Lukas Chavez
11 Variant Calling From Next Generation Sequence Data .  . . . . . . . . . . . . . . 209
Nancy F. Hansen
12 Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes. .. . . . . . . . . . . . . . . . . . . . . 225
Songjoon Baek and Myong-Hee Sung

PART IV TOOLS
13 NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets . . . . . . . . . . . . . . . . . . . . . . . . 243
Marco Antonio Mendoza-Parra, Mohamed-Ashick M. Saleem, Matthias Blum, Pierre-Etienne Cholley, and Hinrich Gronemeyer
14 Operating on Genomic Ranges Using BEDOPS . . . . . . . . . . . . . . . . . . . . . . . . . . 267
Shane Neph, Alex P. Reynolds, M. Scott Kuehn, and John A. Stamatoyannopoulos
15 GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality . . . . . . . . . . . . . . . . . . . . . . 283
Thomas D. Wu, Jens Reeder, Michael Lawrence, Gabe Becker, and Matthew J. Brauer
16 Visualizing Genomic Data Using Gviz and Bioconductor . . . . . . . . . . . . . . . . . . 335
Florian Hahne and Robert Ivanek
17 Introducing Machine Learning Concepts with WEKA . . . . . . . . . . . . . . . . . . . . . 353
Tony C. Smith and Eibe Frank
18 Experimental Design and Power Calculation for RNA-seq Experiments . . . . . . . . . . . . .  . . 379
Zhijin Wu and Hao Wu
19 It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR . . . . . . . 391
Aaron T.L. Lun, Yunshun Chen, and Gordon K. Smyth
Index. . . . . . . . . . . . . . . . . .. . . . . . 417


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