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- Bioconductor
- Bioinformatics and Computational Biology Solutions Using R and Bioconductor
- Bioinformatics and computational biology solutions using R and Bioconductor

## Bioconductor

Carey, Rafael A. This book guides through practical bioinformatics data analysis using the Bioconductor toolkit, which is based on the statistical language R. R itself is an open-source recreation of the language S-Plus. The Bioconductor is a collection of R-packages for the analysis of genomic and molecular biological data generated in high-throughput experiments. High-throughput experiments are characterized by large amounts of data generated in short periods of time on a sizable number of samples.

The book focuses on gene expression microarrays, the high-throughput technology for which statistical methods are best developed today. Each of the discussed experimental technologies is introduced briefly to help even the relative novice reader in bioinformatics to be familiar with them before the discussion dives into the specific data analysis problems and methods. In the same vein, it would have been useful to provide the reader with some introduction to the syntax and semantics of R itself and the coding conventions used in the examples; but unfortunately the book simply writes down R code examples without a guide and even without any obvious systematic or self-explanatory coding style.

The somewhat idiosyncratic short-naming convention, although apparently common-place among R users, does not help to make the code examples more transparent either. So, the reader who chooses this book as a holiday reading on a remote island should be advised to take along a reference book on R, in order to make sense of the many R-code samples provided.

The problem is not restricted to the R-code examples; even the Bioconductor itself is not introduced in enough detail to convey to the reader a systematic understanding of the design and roadmap of this powerful evolving bioinformatics toolkit.

This is a critical loss to those readers who are hoping to engage in the community effort and who need to learn about fundamental data structures and design conventions of the Bioconductor.

For instance, the importance of the exprSet data structure is announced early in the book, and this structure seems to be used in many of the code examples, yet there is not even a tabular or schematic overview provided about it. It is still possible for the reader who has seen many programming languages to follow the discussion, but those readers who lack such experience may be lost.

The strength of the book seems to lie more on a practical application-oriented discussion of the various data analysis approaches with a solid body of explanation, references, and comparison of alternative methods.

It is also helpful that even the practically oriented reader with a more casual background in mathematics will have a chance of following the discussion. However, the book is again not self-consistent even in the methodological discussion, because most methods are being discussed by reference only and their essentials are not actually described either.

The application of these methods is demonstrated using realistic data and there is plenty of example output and diagrams shown that the reader can still follow the point of the discussion albeit somewhat unsure about the specific detail. This is followed by a series of short case studies which illustrate the application of the sum of material discussed in the four main parts to specific example projects. Each part will be useful to the reader as each is an essential component of working with high-throughput data.

Particularly, the parts i, iii, and iv are inherently mathematical, and hence clearly the domain of R-packages and their discussion is most gratifying.

It is good to see R-examples for both statistical applications parts i and iii as well as for discrete mathematics algorithms used in graph theory part iv. Conversely, regarding the treatment of annotation metadata in R part ii , it is not obvious why one would rely solely on R-packages to access such annotations which mostly reside in relational database management systems, XML or web resources, integrating these resources does not seem to be such a unique strength of R.

It would have been helpful if the book had discussed how the user can make their own annotation database resources accessible to analysis algorithms executed in R rather than simply showing some subset of R-modules designed to interact with the world outside.

Likewise the short discussion on workflow integration of R-analyses has too narrow a horizon, is caught within the perimeter of R, not discussing any other alternatives such as use of R as part of a larger analysis platform. The quality of the discussion and relevant details is exceptional, which can be seen in the reviewer's favorite chapter on visualization techniques, and overall the book is very nicely produced with plenty of high-quality illustrations.

It is also good to know that in fact the content of the book was actually generated using the R-package itself and that the examples and code can be downloaded from a companion website. Indeed, one intriguing use of the book might be as a main textbook for a Bioinformatics course, which would guide the student through practical tasks and methodology leaving plenty of room for the additionally desirable self-study in primary methodological literature and software reference manuals.

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Skip Nav Destination Article Navigation. Close mobile search navigation Article Navigation. Volume 8. Article Contents. Article Navigation. Schadow Schadow. Oxford Academic. Google Scholar. Select Format Select format.

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## Bioinformatics and Computational Biology Solutions Using R and Bioconductor

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## Bioinformatics and computational biology solutions using R and Bioconductor

Du kanske gillar. Ladda ned. Spara som favorit. Skickas inom vardagar. Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology.

*The Bioconductor interfaces to machine learning tools are described and illustrated. Key problems of model selection and interpretation are reviewed in examples.*

#### Fler böcker av författarna

Carey, Rafael A. This book guides through practical bioinformatics data analysis using the Bioconductor toolkit, which is based on the statistical language R. R itself is an open-source recreation of the language S-Plus. The Bioconductor is a collection of R-packages for the analysis of genomic and molecular biological data generated in high-throughput experiments. High-throughput experiments are characterized by large amounts of data generated in short periods of time on a sizable number of samples.

With the fast development of high throughput technologies such as microarray and next generation sequencing NGS , bioinformatics becomes an essential part of biomedical research on human diseases. Analysis of the large amount of high throughput data becomes the new bottleneck in many research projects. The goal of this course is to let students get familiar with the commonly used bioinformatics data analysis tools via hands-on training and discussion on both classical and state-of-the-art literature. The topics include analysis and visualization of both microarray and NGS data for genotyping, and epigenomics, and transcriptome studies in human diseases as well as advanced methods based on gene network inference and analysis. Grading Assistant : Instructors.

It seems that you're in Germany. We have a dedicated site for Germany. Editors: Gentleman , R. Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including. The developers of the software, who are in many cases leading academic researchers, jointly authored chapters.

Тишина шифровалки взорвалась сигналом тревоги, доносившимся из служебного помещения ТРАНСТЕКСТА.

Соши заливалась слезами. - Джабба, - спросил Фонтейн, - много они похитили. - Совсем мало, - сказал Джабба, посмотрев на монитор. - Всего лишь какие-то обрывки, в полном виде - .

*Включился звук, и послышался фоновой шум.*

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data.

Covers the basics of R software and the key capabilities of the Bioconductor project a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology and rooted in the open source statistical computing environment R , including importation and preprocessing of high-throughput data from microarrays and other platforms.