This book is a comprehensive guide to all of the mathematics, statistics and computing you will need to successfully operate DNA microarray experiments. It is written for researchers, clinicians, laboratory heads and managers, from both biology and bioinformatics backgrounds, who work with, or who intend to work with microarrays. The book covers all aspects of microarray bioinformatics, giving you the tools to design arrays and experiments, to analyze your data, and to share your results with your organisation or with the international community. There are chapters covering sequence databases, oligonucleotide design, experimental design, image processing, normalisation, identifying differentially expressed genes, clustering, classification and data standards. The book is based on the highly successful Microarray Bioinformatics course at Oxford University, and therefore is ideally suited for teaching the subject at postgraduate or professional level.

This book provides a comprehensive, interdisciplinary collection of the main, up-to-date methods, tools, and techniques for microarray data analysis, covering the necessary steps for the acquisition of the data, its preprocessing, and its posterior analysis. Featuring perspectives from biology, computer science, and statistics, the volume explores machine learning methods such as clustering, feature selection, classification, data normalization, and missing value imputation, as well as the statistical analysis of the data and the most popular computer tools to analyze microarray data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will aid researchers in getting successful results. Cutting-edge and authoritative, Microarray Bioinformatics serves as an ideal guide for researchers and graduate students in bioinformatics, with basic knowledge in biology and computer science, and with a view to work with microarray datasets.

Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever possible, the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression. Microarrays are an automated way of carrying out thousands of experiments at once, and allows scientists to obtain huge amounts of information very quickly Short, concise text on this difficult topic area Clear illustrations throughout Written by well-known teachers in the subject Provides insight into how to analyse the data produced from microarrays

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 BiologyTM 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.

Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis is one of the first books dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods ranging from data normalization, feature selection and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis focuses on two well-known data sets, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.

Modern DNA microarray technologies have evolved over the past 25 years to the point where it is now possible to take many million measurements from a single experiment. These two volumes, Parts A & B in the Methods in Enzymology series provide methods that will shepard any molecular biologist through the process of planning, performing, and publishing microarray results. Part A starts with an overview of a number of microarray platforms, both commercial and academically produced and includes wet bench protocols for performing traditional expression analysis and derivative techniques such as detection of transcription factor occupancy and chromatin status. Wet-bench protocols and troubleshooting techniques continue into Part B. These techniques are well rooted in traditional molecular biology and while they require traditional care, a researcher that can reproducibly generate beautiful Northern or Southern blots should have no difficulty generating beautiful array hybridizations. Data management is a more recent problem for most biologists. The bulk of Part B provides a range of techniques for data handling. This includes critical issues, from normalization within and between arrays, to uploading your results to the public repositories for array data, and how to integrate data from multiple sources. There are chapters in Part B for both the debutant and the expert bioinformatician. Provides an overview of platforms Includes experimental design and wet bench protocols Presents statistical and data analysis methods, array databases, data visualization and meta analysis