Statistical Genomics: Methods and Protocols

ISBN-13: 9781493935765 / Angielski / Twarda / 2016 / 418 str.

Ewy Mathe; Sean Davis

Opis

This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls.

Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data.

Recenzje

"This collection of articles offers a thorough overview of the field, making it an opportune and useful addition to the literature. The book is written in an accessible language and the variety of the topics which are presented recommends it as an excellent starting point or updated reference of the field. It is suitable for both post-graduate and established researchers, and the numerous examples that accompany the discussed topics recommend it as an asset." (Irina Ioana Mohorianu, zbMATH 1346.92003, 2016)

Spis treści

Part I Groundwork

 1.   Overview of Sequence Data Formats
Hongen Zhang

 2.   Integrative Exploratory Analysis of Two or More Genomic Datasets
Chen Meng and Aedin Culhane       

 3.   Study Design for Sequencing Studies
Loren Honaas, Naomi Altman, and Martin Krzywinski

 4.   Genomic Annotation Resources in R/Bioconductor
Marc RJ Carlson, Hervé Pagès, Sonali Arora, Valerie Obenchain, and Martin Morgan

Part II Public Genomic Data

5.  The Gene Expression Omnibus Database
Emily Clough and Tanya Barrett

6.  A Practical Guide to the Cancer Genome Atlas (TCGA)    
Zhining Wang, Mark A. Jensen, and Jean Claude Zenklusen

 

Part III Applications

7.    Working with Oligonucleotide Arrays
Benilton S. Carvalho

8.    Meta-Analysis in Gene Expression Studies
Levi Waldron and Markus Riester

9.    Practical Analysis of Genome Contact Interaction Experiments
Mark A. Carty and Olivier Elemento

10. Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data
Matthias Lienhard and Lukas Chavez

11. Variant Calling From Next Generation Sequence Data
Nancy F. Hansen

12. Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes
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
Marco Antonio Mendoza-Parra, Mohamed-Ashick M. Saleem, Matthias Blum, Pierre Etienne Cholley, and Hinrich Gronemeyer

14. Operating on Genomic Ranges Using BEDOPS
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
Thomas D. Wu, Jens Reeder, Michael Lawrence, Gabe Becker, and Matthew Brauer

16. Visualizing Genomic Data using Gviz and Bioconductor
Florian Hahne and Robert Ivanek

17. Introducing Machine Learning Concepts with WEKA
Tony C. Smith and Eibe Frank

18. Experimental Design and Power Calculation for RNA-Seq Experiments
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
Aaron T.L. Lun, Yunshun Chen, and Gordon K. Smyth