Bioconductor Case Studies

Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis. Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.

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.

After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions

This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.

The Affymetrix GeneChip® system is one of the most widely adapted microarray platforms. However, due to the overwhelming amount of information available, many Affymetrix users tend to stick to the default analysis settings and may end up drawing sub-optimal conclusions. Written by a molecular biologist and a biostatistician with a combined decade of experience in practical expression profiling experiments and data analyses, Gene Expression Studies Using Affymetrix Microarrays tears down the omnipresent language barriers among molecular biology, bioinformatics, and biostatistics by explaining the entire process of a gene expression study from conception to conclusion. Truly Multidisciplinary: Merges Molecular Biology, Bioinformatics, and Biostatistics This authoritative resource covers important technical and statistical pitfalls and problems, helping not only to explain concepts outside the domain of researchers, but to provide additional guidance in their field of expertise. The book also describes technical and statistical methods conceptually with illustrative, full-color examples, enabling those inexperienced with gene expression studies to grasp the basic principles. Gene Expression Studies Using Affymetrix Microarrays provides novices with a detailed, yet focused introductory course and practical user guide. Specialized experts will also find it useful as a translation dictionary to understand other involved disciplines or to get a broader picture of microarray gene expression studies in general. Although focusing on Affymetrix gene expression, this globally relevant guide covers topics that are equally useful for other microarray platforms and other Affymetrix applications.

This book presents the application of genomic tools to examine bacterial adaptation. The emphasis is on data analysis and interpretation.

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.

Using chips composed of thousands of spots, each with the capability of holding DNA molecules corresponding to a given gene, DNA microarray technology has enabled researchers to measure simultaneously gene expression across the genome. As with other large-scale genomics approaches, microarray technologies are broadly applicable across disciplines of life and biomedical sciences, but remain daunting to many researchers. This guide is designed to demystify the technology and inform more biologists about this critically important experimental technique. Cohesive overview of the technology and available platforms, followed by detailed discussion of experimental design and analysis of microarray experiments Up-to-date description of normalization methods and current methods for sample amplification and labeling Deep focus on oligonucleotide design, printing, labeling and hybridization, data acquisition, normalization, and meta-analysis Additional uses of microarray technology such as ChIP (chromatin immunoprecipitation) with hybridization to DNA arrays, microarray-based comparative genomic hybridization (CGH), and cell and tissue arrays