Next Generation Microarray Bioinformatics: Methods and Protocols

ISBN-13: 9781617793998 / Angielski / Twarda / 2011 / 420 str.

Junbai Wang; Aik Choon Tan; Tianhai Tian

Opis

Recent improvements in the efficiency, quality, and cost of genome-wide sequencing have prompted biologists and biomedical researchers to move away from microarray-based technology to ultra high-throughput, massively parallel genomic sequencing (Next Generation Sequencing, NGS) technology. In Next Generation Microarray Bioinformatics: Methods and Protocols, expert researchers in the field provide techniques to bring together current computational and statistical methods to analyze and interpreting both microarray and NGS data. These methods and techniques include resources for microarray bioinformatics, microarray data analysis, microarray bioinformatics in systems biology, next generation sequencing data analysis, and emerging applications of microarray and next generation sequencing. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory.Authoritative and practical, Next Generation Microarray Bioinformatics: Methods and Protocols seeks to aid scientists in the further study of this crucially important research into the human DNA.

Spis treści

TABLE OF CONTENTS

 PREFACE

 LIST OF CONTRIBUTORS

 I) Introduction and Resources for Microarray Bioinformatics

 1.      A Primer on the Current State of Microarray Technologies

Alexander J. Trachtenberg, Robert J. Chang, Azza E. Abdalla, Andrew Fraser, Steven Y. He, Jessica N. Lacy, Chiara Rivas-Morello, Allison Truong, Gary Hardiman, Lucila Ohno-Machado, Fang Liu, Eivind Hovig and Winston Patrick Kuo

2.      The KEGG Databases and Tools Facilitating Omics Analysis: Latest Developments Involving Human Diseases and Pharmaceuticals

Masaaki Kotera, Mika Hirakawa, Toshiaki Tokimatsu, Susumu Goto and Minoru Kanehisa

3.    Strategies to Explore Functional Genomics Data Sets in NCBI’s GEO Database

Stephen E. Wilhite and Tanya Barrett

 II) Microarray Data Analysis (Top down approach)

 4.      Analyzing Cancer Samples with SNP Arrays

        Peter Van Loo, Gro Nilsen, Silje H. Nordgard, Hans Kristian Moen Vollan, Anne-                Lise Børresen-Dale, Vessela N. Kristensen and Ole Christian Lingjærde

5.    Classification Approaches for Microarray Gene Expression Data Analysis

Leo Wang-Kit Cheung

6.    Biclustering of Time Series Microarray Data

Jia Meng and Yufei Huang

7.    Using the Bioconductor GeneAnswers Package to Interpret Gene Lists

Gang Feng, Pamela Shaw, Steven T. Rosen, Simon M. Lin and Warren A. Kibbe

8.    Analysis of Isoform Expression from Splicing Array using Multiple Comparisons

T. Murlidharan Nair

9.    Functional Comparison of Microarray Data across Multiple Platforms Using the Method of Percentage of Overlapping Functions

Zhiguang Li, Joshua C. Kwekel and Tao Chen

10.  Performance Comparison of Multiple Microarray Platforms for Gene Expression Profiling

Fang Liu, Winston P. Kuo, Tor-Kristian Jenssen and Eivind Hovig

11.  Integrative Approaches for Microarray Data Analysis

Levi Waldron, Hilary A. Coller and Curtis Huttenhower

 

III) Microarray Bioinformatics in Systems Biology (Bottom up approach)

 

12.  Modelling Gene Regulation Networks using Ordinary Differential Equations

Jiguo Cao, Xin Qi and Hongyu Zhao

13.  Non-homogeneous Dynamic Bayesian Networks in Systems Biology

Sophie Lèbre, Frank Dondelinger and Dirk Husmeier

14.  Inference of Regulatory Networks from Microarray Data with R and the Bioconductor Package qpgraph

Robert Castelo and Alberto Roverato

15.  Effective Nonlinear Methods for Inferring Genetic Regulation from Time-series Microarray Gene Expression Data

Junbai Wang and Tianhai Tian

 

IV) Next Generation Sequencing Data Analysis

 

16.  An Overview of the Analysis of Next Generation Sequencing Data

Andreas Gogol-Döring and Wei Chen

17.  How to Analyze Gene Expression using RNA-Sequencing Data

Daniel Ramsköld, Ersen Kavak and Rickard Sandberg

18.  Analyzing ChIP-seq Data: Preprocessing, Normalization, Differential Identification and Binding Pattern Characterization

Cenny Taslim, Kun Huang, Tim Huang and Shili Lin

19.  Identifying Differential Histone Modification Sites from ChIP-seq Data

Han Xu and Wing-Kin Sung

20.  ChIP-Seq Data Analysis: Identification of Protein-DNA Binding Sites with SISSRs Peak Finder

Leelavati Narlikar and Raja Jothi

21.  Using ChIPMotifs for de novo Motif Discovery of OCT4 and ZNF263 based on ChIP-based High-throughput Experiments

Brian A. Kennedy, Xun Lan, Tim H-M. Huang, Peggy J. Farnham and Victor X. Jin

 

V) Emerging Applications of Microarray and Next Generation Sequencing

 

22.  Hidden Markov Models for Controlling False Discovery Rate in Genome-Wide Association Analysis

Zhi Wei

23.  Employing Gene Set Top Scoring Pairs to Identify Deregulated Pathway-Signatures in Dilated Cardiomyopathy from Integrated Microarray Gene Expression Data

Aik Choon Tan

24.  JAMIE: A Software Tool for Jointly Analyzing Multiple ChIP-chip Experiments

Hao Wu and Hongkai Ji

25.  Epigenetic Analysis: ChIP-chip and ChIP-seq

Matteo Pellegrini and Roberto Ferrari

26.  BiNGS!SL-seq: A Bioinformatics Pipeline for the Analysis and Interpretation of Deep Sequencing Genome-wide Synthetic Lethal Screen

Jihye Kim and Aik Choon Tan