Book Description

Nucleases, enzymes that restructure or degrade nucleic acid polymers, are vital to the control of every area of metabolism. They range from "housekeeping" enzymes with broad substrate rangesto extremely specific tools. Many types of nucleases are used in lab protocols, and their commercial and clinical uses are expanding.The purpose of this book is to introduce the reader to some well characterizedprotein nucleases, and the methods used to determinetheir activity, structure, interaction with other molecules, and physiological role. Each chapter begins with a mini-review on a specific nuclease or a nuclease-related theme. Although many chapters cover several topics, they were arbitrarily divided into five sections:

-Characterizing nuclease activity, which includes protocols and assaysto determine general (processive, distributive) or specific mechanisms.Methods to assay nuclease products, identify cloned nucleases and determine their physiological role are also included here.

-Inhibitors and activators of nucleases, summarizes assays for measuring the effects of other proteins and small molecules.Many of these inhibitors have clinical relevance.

-Relating nuclease structure and function, gives an overview of methods to determine or model the 3D structure of nucleases and their complexes with substrates and inhibitors. A 3-D structure can greatly aid the rational design of nucleases and inhibitors for specific purposes.

-Nucleases in the clinic summarizes assays and protocolssuitable for use with tissues and for nuclease based therapeutics.

-Nucleases in the lab includes protocolsto use nucleases in cloning and determining the activity of other proteins.

The experienced reader will immediately recognize several of the nucleases used as examples throughout this book, especially RNase A and restriction enzymes. However, new nucleases with novel specificity are constantly being discovered, performing often unexpected functions.A regulator of the unfolded protein response, identified initially as a kinase, is also a sequence specific ribonuclease (3). A human analogue of a plant ribonuclease was discovered in the search for a tumor suppressor protein (7) while angiogenin (25) was cloned as a factor stimulating blood vessel formation. RNase L is one of the mediators of Interferon activity (12).

Researchers who unmask a nuclease disguised as a cytokine, mating factor, toxin, etc.should find the methods for characterizing their protein described in the first section of this book particularly useful. These chapters suggest questions to ask about the nuclease's activity or primary structure.Is the amino acid sequence novel or similar to one of the major families of nucleases(7, 18)?Is the cleavage processive or distributive, i.e., does the nuclease scan the nucleic acid polymer and cleave repeatedly before separating, as has been shown for restriction endonucleases and glycosylases (1) or, does it, in the fashion of RNase A (2), cleave and simultaneously release the polymer, generating products that are at the same time novel substrates?Does the back (synthesis) reaction affect the kinetics of the cleavage process? Is the nuclease activity essential for metabolic activity, asMcClure and coworkers (5) have elegantly demonstrated for the stylar RNases?Finally, is the activity sensitive to known inhibitors or activators ? (8,9,12)

The chapters at the center of this book were selected to introduce the reader to methods used to define the tertiary structure of nucleases.Of course, a complete tertiary structure determination by X-ray crystallography (13,14,17,19,20) or NMR (16) requires a good deal of time and specialized techniques .However,if the sequence has significant identity to a protein for which a structure has been determined, tools now available on the Internet allow one to model the probable 3D structure (18). The methods described aid in the design of nucleases with new properties (15,20) and improved inhibitors (13,14).

Nuclease based therapies and diagnosticsare coming into the clinic. DNase I therapy (20,21) has improved the lives of thousands of cystic fibrosis victims. Nucleases with demonstrated antitumor activity (24) stimulated clinical trials of other members of the RNase A family and modified forms(23, 26).Better understanding of nucleases that repair damaged DNA (1,18), mediate retroviral integration and replication (10,22),or play a role in cytokine and growth factor mechanisms (9,12,25) is important both in understanding disease progression and developing better therapeutic modalities.Antisense therapies, for example, depend on directing the activity of intracellular RNase H (11).

Finally, nucleases are a major research tool in molecular biology.The exquisite specificity of restriction endonucleases (19,27,28) is routinely used in gene cloning.Exploiting the special qualities of a subclass, hapaxoterminers, can make subcloning and gene modification easier (29).There are many uses for non-specific nucleases as well.DNase I is used to locate the binding sites of proteins on DNA (30) and S1-nuclease (31) or ribonuclease (32) mapping to quantitate specific mRNAs. Degradation of nucleic acid polymers with non-specific nucleases, including DNase I, RNase A and the endonuclease from Serratia (17) are used to clarify lysates and ease protein purification.

The comments and notes on the method provide guidance and insight when things go wrong (i.e., not as planned), and for how to go about correcting them.

We can anticipate thatthe model proteins used to develop new biophysical methods and clinical therapies, which have changed little in the past 30 years, will show more variety in the future. Many new nucleases arecommercially available, and the genome projects are revealing copious sequence information about nuclease families that may be more important metabolically.The examples in this book, as varied as they are, are only starting points for exploration in the wide world of nucleases.

Numbers in parentheses refer to chapters in the book ... Read more

Book Description

Life science data integration and interoperability is one of the most challenging problems facing bioinformatics today.In the current age of the life sciences, investigators have to interpret many types of information from a variety of sources: lab instruments, public databases, gene expression profiles, raw sequence traces, single nucleotide polymorphisms, chemical screening data, proteomic data, putative metabolic pathway models, and many others. Unfortunately, scientists are not currently able to easily identify and access this information because of the variety of semantics, interfaces, and data formats used by the underlying data sources.

Bioinformatics: Managing Scientific Data tackles this challenge head-on by discussing the current approaches and variety of systems available to help bioinformaticians with this increasingly complex issue.The heart of the book lies in the collaboration efforts of eight distinct bioinformatics teams that describe their own unique approaches to data integration and interoperability. Each system receives its own chapter where the lead contributors provide precious insight into the specific problems being addressed by the system, why the particular architecture was chosen, and details on the system's strengths and weaknesses. In closing, the editors provide important criteria for evaluating these systems that bioinformatics professionals will find valuable.

* Provides a clear overview of the state-of-the-art in data integration and interoperability in genomics, highlighting a variety of systems and giving insight into the strengths and weaknesses of their different approaches.
* Discusses shared vocabulary, design issues, complexity of use cases, and the difficulties of transferring existing data management approaches to bioinformatics systems, which serves to connect computer and life scientists.
* Written by the primary contributors of eight reputable bioinformatics systems in academia and industry including: BioKris, TAMBIS, K2, GeneExpress, P/FDM, MBM, SDSC, SRS, and DiscoveryLink. ... Read more

Reviews (1)

Great contributions, lots of details, in-depth knowledge
The book compiles chapters written by different authors all of whom are experts in the area of managing life science and/or scientific data. The variety of approaches, the clear description, and the details make this book an important source for those who want to know more about the "state-of-the-art" data management in the area of bioinformatics. By presenting such a great mixtures the reader can reflect on hes/her own requirements thus using this book as a first step for making a decision how to manage one's own data in this realm.

Those who are active in managing life science data will find this book to be a great resource and a great starting point to get a comprehensive overview of alternative solutions and their strength and weaknesses. ... Read more

Book Description

The completion of the first draft of the human genome has led to an explosion of interest in genetics and molecular biology.The view of the genome as a network of interacting computational components is well-established, but researchers are now trying to reverse the analogy, by using living organisms to construct logic circuits.The potential applications for such technologies is huge, ranging from bio-sensors, through industrial applications to drug delivery and diagnostics.This book would be the first to deal with the implementation of this technology, describing several working experimental demonstrations using cells as components of logic circuits, building toward computers incorporating biological components in their functioning. ... Read more

Book Description

Bioinformatics and computational intelligence are undoubtedly remarkably fast growing fields of research and real-world applications with enormous potential for current and future developments. Bioinformatics Using Computational Intelligence Paradigms contains recent theoretical approaches and guiding applications of biologically inspired information processing systems (computational intelligence) against the background of bioinformatics. This carefully edited monograph combines the latest results of bioinformatics and computational intelligence, and offers promising cross-fertilization and interdisciplinary work between these growing fields.

Book Description

This book is a practical introduction into Unix/Linux and its programming to biologists as well as to chemists and physicists who work in bioinformatics and biophysics. The goal is to learn about the power of the stream editor 'sed' and the programming languages 'awk' and 'perl' in order to extract or format information from various sources. It is written for beginners with no computational but biological background. Basic programming constructs are introduced  and applied. With this book, the reader will be able to work in the Unix environment (BSD; Linux, MacOSX) and to write programs in order to format and analyse large data files. ... Read more

Book Description

This book provides insight into all important fields in bioinformatics including sequence analysis, expression analysis, structural biology, proteomics and network analysis. Many of the leading scientists in the field have contributed chapters to topics of which range from genome sequence determination and its analysis, to the analysis of transcripts and proteins with the final aim of gaining a deeper understanding of the complex networks cells must obey to in order to live. The book has been compiled for the increasing number of scientists and researchers working in bioinfomatics and genome analysis worldwide who would like not only to get an overview but who also enjoy reading about the latest results in this exciting field. ... Read more

Book Description

Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.

* Provides the background mathematics required to understand why certain algorithms work
* Guides the reader through probability theory, entropy and combinatorial optimization
* In-depth coverage of molecular biology and protein structure prediction
* Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
* Includes class tested exercises useful for self-study
* Source code of programs available on a Web site

Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields. ... Read more

Reviews (3)

Don't start with this book
In general I agree with the two previous reviews.

This book is not very good as an introduction. First read some other book such as Setubal and Meidanis, "Introduction to Computational Molecular Biology"; or Krane & Raymer, "Fundamental Concepts of Bioinformatics". These books have more readable narrative and examples.

The writing in this book is obtuse. It is written like an advanced abstract math book, not like an ostensibly applied science book. The notation is unnecessarily intricate. Even though it says "Introduction" in the title, there are very few tutorial examples. This is just for mathematicians/computer scientists: no biologist I have ever known would/could read this and really understand the algorithms.

This book does, however, have one of the more complete detailed descriptions of various algorithms used for sequence matching, etc. If you have read some other books and are looking for more details on algorithms, then this is your book. But I'm still waiting for THE ultimate Computational Biology book!

Unsuitable for its stated purpose.
The book purports to be a "self-contained introduction" to computational biology. It fails on both counts due to its excessive ambition, its opaque pedagogy, and a large number of significant typographical errors, such as entire subroutines missing from pseudocode examples. Undergraduates seeking an accessible survey are advised to look elsewhere.

That said, the mathematical rigor of the text makes it ideal for students who have moved beyond the need for accessible surveys and wish to improve their fundamental understanding of the field.

Interesting but not very good for beginners
This is an unusual book. The authors obviously have not been aquinted with biomolecular sequence analysis and fail to give state-of-the-art references to research work in this field. The same comment applies to the description of applications of Shannon communication theory to DNA and protein sequence analysis. The enormous impact of these applications in the 1970s, 1980s and 1990s is not reflected in the book and one could wonder why the authors bother to write of Shannon theory at all. In addition to the above misgivings the authors decided to confuse the reader by including a discussion of quite controversial relationship between Shannon entropy and thermodynamic entropy. Both computational and laboratory biologists will not benefit from this kind of confusion. Mathematicians and computer scientist will probably be mislead by a superficial treatment of this quite intricate topic. Physicists and chemist will probably be able to sort out useful information from over-interpretations but they may wonder why this issue is discussed in a computational biology text.

Despite the above critique I like the book. Organization of this text is interesting and distinctly different form other books in the field. Chapters on sequence alignment and phylogenetic trees are most interesting and original. They should probably be read in conjunction with more systematic textbooks such as Gusfield's "Algorithms on strings, trees and sequences" or Li's "Molecular evolution." Despite many misgivings (see the beginning paragraph of this review) the mathematical primer (chapter 2) is very much worth reading for its originality and compactness. Particularly sections about probability distributions and combinatorial optimization can be useful for non-mathematicians and interesting for those who are mathematically literate. However, care should be exercised (see the beginning paragraph) while reading sections about entropy and about optimality of the genetic code. Chapter 1 about principles of molecular biology is not very good for non-biologists because it is too compact. Chapter about structure prediction is also too compact to be either understandable to non-specialists or enjoyable by the experts. If the authors' ambitious approach was to be sustained, this chapter should probably be expanded to the size of entire book. Exercises at the end of every chapter of the book are interesting and worth the reader's attention. It would probably be good to have access to solutions of all exercises but it is a minor problem.

In summary: it is an interesting book but it should be read in conjunction with other texts. It should not be recommended to the beginners in computational biology. Mathematically seasoned readers will enjoy reading selected parts of this book. It would be nice if the publisher could consider lowering price of this book (already in paperback.) ... Read more

Book Description

This book constitutes the refereed joint proceedings of six workshops on evolutionary computing, EvoWorkshops 2005, held in Lausanne, Switzerland in March/April 2005.

The 56 revised full papers presented were carefully reviewed and selected from a total of 143 submissions. In accordance with the six workshops covered, the papers are organized in topical sections on evolutionary bioinformatics; evolutionary computing in communications, networks, and connected systems; hardware optimization techniques; evolutionary computation in image analysis and signal processing; evolutionary music and art; and evolutionary algorithms in stochastic and dynamic environments.

Book Description

In the current era of complete genome sequencing, Bioinformatics and Molecular Evolution provides an up-to-date and comprehensive introduction to bioinformatics in the context of evolutionary biology.This important textbook will equip readers with a thorough understanding of the quantitative methods used in the analysis of molecular evolution, and will be essential reading for advanced undergraduates, graduates, and researchers in molecular biology, genetics, genomics, computational biology, and bioinformatics courses. ... Read more

Book Description

Functional genomics--the deconstruction of the genome to determine the biological function of genes and gene interactions--is one of the most fruitful new areas of biology. The growing use of DNA microarrays allows researchers to assess the expression of tens of thousands of genes at a time. This quantitative change has led to qualitative progress in our ability to understand regulatory processes at the cellular level.

This book provides a systematic introduction to the use of DNA microarrays as an investigative tool for functional genomics. The presentation is appropriate for readers from biology or bioinformatics. After presenting a framework for the design of microarray-driven functional genomics experiments, the book discusses the foundations for analyzing microarray data sets, genomic data-mining, the creation of standardized nomenclature and data models, clinical applications of functional genomics research, and the future of functional genomics. ... Read more

Reviews (7)

Well written
This is a well written book that gives an overview of the technology of microarrays and their use as investigative tools in functional genomics experiments. I found the technical and analytical descriptions very easy to follow. This is still the only book around that can bring any investigator with little knowledge of molecular biology, data analysis, and/or microarrays up to speed in the field. It is also a good text book for a graduate level course on microarray data analysis.

Not well written...
I am not an informatics researcher, however I hold a doctorate in biotechnology related areas, as well a law degree. I routinely purchase books and journals to keep up. However, the problem with this book is its presentation. It is written in an almost stereotypically pretentious manner to the extent that it clearly detracts from the subject matter's presentation. Did you know that a tissue or cell type may be "interrogated"? Coincedentally, I happened upon a brief review article by the same author in Nature Biotech. Again the writing was such that it was too much of an effort to extract what was being said. For those who feel drawn to this book, check the internal pages on Amazon's site.

lots of important stuff
This book contains lots of important topical information on the design and analysis of microarray experiments. It calls attention to a lot of important but sometimes subtle issues that many biologists appear to be overlooking. It appears to be a must-read for researchers who want to avoid expensive dead ends. But it's not perfect...

A well-informed computer scientist will recognize that quite a few computational statements are just plain wrong (e.g., p 180,
"[Dendrograms] require the comprehensive precomputation of the dissimilarity measure for all pairs of genes, which grows on the order of N^2" Wrong! Try bucketing. Or p 139, a dissimilarity function based on linear correlation coefficients is "definite". No! If x is a vector and C is a scalar, then clearly x=/=Cx, but d(x,Cx)=0, contrary to the definition of "definite". The "pseudocode" in Chapter 4 is not any clearer than the text, and it is not structured in a way that would allow it to be elaborated into well-engineered code. So rely on this book for big ideas and references, not for details. The book also reinforces my preconception that MIT Press doesn't employ editors... 'way too many typos, for starters.

You have to know the basics of molecular biology for this book, and it wouldn't hurt to have a basic understanding of DNA chips as well. It's definitely not the first step for a mathematical scientist hoping to become a bioinformatician. (But why should it be? :c)

Amazing
This is the book we have all been waiting for. The authors do an amazing job of describing, in understandable terms, how to perform meaningful microarray experiments. I highly recommend this seminal work.

The masters' secrets unveiled
This book is an excellent educational source for the rapidly exploding field of bioinformatics, particularly in the area of functional genomics- i.e. teasing out the functions of the thousands of genes in the genome. Described in clear detail are many approaches to the complex analysis of RNA expression array data, including the appropriate situations for use and advantages and pitfalls inherent in each approach. The reader will learn biological theory integrated with mathematical concepts. The authors also clearly have a lucid understanding of the technical strengths and weaknesses of microarray technology. ... Read more

Book Description

This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications. ... Read more

Reviews (1)

different methods for finding clusters
The book has a nice treatment of the problem of finding, in some sense, clusters in data. Several papers point out that there is often some subjectivity here, as to which data sits in a particular cluster. Fuzziness in the boundary of a cluster. It can depend on what your underlying model is.

Possibly of interest to some is work on high dimensionality data, and trying to find clusters in these. Even visualisations might be non-trivial.

The book has value in letting you see a variety of ideas for finding clusters. Perhaps some of these might prove germane to your research. ... Read more

Book Description

Praise for Digital Code of Life

"The man who wrote the best history yet of the open-source movement in Rebel Code gives us an equally lucid and penetrating look at bioinformatics. Well done!"
–Eric S. Raymond
Author of The Cathedral and the Bazaar

"This book provides a riveting account of the history of bioinformatics and of the manner in which bioinformatics has contributed to advancing our knowledge of the human genome. Glyn Moody has chronicled through reviews of key scientific papers and through interviews with leading scientists, the major developments in the field of genomics in the past half century, from the discovery of the double helix to the emergence of proteomics, pointing to their relevance to science, medicine, and industry and to the critical contributions of bioinformatics."
–Sam Hanash, University of Michigan
President of The Human Proteome Organisation ... Read more

Reviews (1)

Excellent Laymen's overview
Digital Code of Life is an excellent overview of the convergence of IT and life sciences which has occurred nearly overnight. In the space of a few short years, the human genetic code has been mapped, and we now are seeing how this will play out for healthcare, drug development, and the marketplace. If you are interested in getting a sense of what is behind all the headlines, it's a worthwhile read. ... Read more

Book Description

Genome science as a discipline is less than five years old, but has already spawned a revolution in the way in which genetics is taught in universities, approached by academic researchers, and applied by pharmaceutical and agricultural research companies. While over a dozen new journals devoted to genomics have appeared in the last few years, no introductory textbook that covers the range of disciplines incorporated into genome science has materialized, until now. A Primer of Genome Science bridges the gap between standard genetics textbooks and highly specialized, technical, and advanced treatments of the subdisciplines. It provides an affordable and up-to-date introduction to the field that is suited to advanced undergraduate or early graduate courses. Bioinformatic principles and experimental strategies are explained side-by-side with the experimental methods, establishing a framework that allows teachers to explore topics and the literature at their own pace.

The Primer is organized into six chapters. Each chapter includes: exercises that can be worked by students using the internet and freely available software for analysis of genomic data; discussion questions; a summary; and suggestions for further reading. An Appendix includes a glossary of terms, with a brief review of key genetic concepts. ... Read more

Reviews (3)

Poorly Written, Good graphics
The book is very poorly written and is too difficult to follow to be called a "primer." Authors often focus on actual software tools and how to use them rather than the science behind them.

To be fair, the graphics offered in the book are excellent and sometimes are the only way to understand a difficult concept.

The preface says to be familiar with "the content of a typical 300 level undergraduate course in genetics" -- it should be a definite prerequisite for reading this book.

The future may view this text as a foundation for GS
Every technician and/or PI should own a copy of this text for their lab. With the logical diagrams and full explanation of the text, this book is really condensed and assumes some knowledge of molecular biology. This book does not assume knowledge of genomics, but rather serves as a manual.

Excellent overview of Functional and Structural Genomics
This somewhat understated book may be overlooked based on its title, and yet it represents the best book currently in print to provide a solid overview of the science and issues in genome science, functional and structural genomics, and the subdiscipline proteomics. Chapter 1 describes current progress with mapping genomes, including the human genome and other genomes in plants and animals. Chapter 2 describes sequencing approaches and gene identification. Chapter 3 deals with gene expression and technologies. Chapter 4 focuses on proteomics including brief introductions to 2D-PAGE and mass spectrometry. This chapter also briefly introduces the reader to structural genomics, or the prediction of protein structure based on sequence through threading and modeling. After a chapter on single nucletide polymorphisms and genotyping the book concludes with a chapter on integrating genome studies including the use of in silico approaches.
Although scant in detail in parts, a major strength of the book is the wide coverage given to science of genomics and its offshoots. Overall an excellent course text for undergraduate or early postgraduate students or others interested in these emerging disciplines. I am not aware of any competing texts which such coverage and certainly not at the price of this one. ... Read more

Book Description

With the explosion of sequence data in public and private databases and the coming explosion of gene expression data in a similar vein, it is becoming increasingly important to understand how to apply well-established data analysis and data classification methods that have been developed in other fields to this field---to try to make sense of the data, to glean biological insights from it, to categorize the data, and to put all of these to good use in industrial applications.

This book introduces the main methods of data analysis and of data classification--as applied to sequence and gene expression analysis--to the biologist and to the computer scientist in this field. It contains material that is presently being taught by the author in the course Data Analysis, Modeling, and Visualization for Bioinformatics at the University of California, Santa Cruz Extension to workers in the biotechnology industry in Silicon Valley. ... Read more

Reviews (9)

This is like a lecture notes
This book has been used at UCSC extension, and seems most of the students are using this book like a lecture/summary notes, not as a full textbook. Without attending the class, it is VERY difficult to understand. However, once you attended the class (and study hard with some supplement textbooks if you are a beginner), you will realize that this book summarizes key points very well.
So... if you are a beginner to this field, you might want to look for an alternative. If you are already comfortable with statistics and bioinformatics, and want to check-out key points, maybe you are interested.

Not even close to Data Analysis or Classification
Do yourself a favour, don't buy this book. You can sit at home and search internet for bioinformatics and scratch with Microsoft word and you might end up with better notes.

Not worth it if it was half the price.
I bought the book for one of Dr. Jagota's classes at UCSC Extension and to say the least, I am very disappointed. The book is no more then an incomplete collection of his notes that he uses for one of his classes. There are very few complete examples and he uses terminology with out explanation, assuming the user knows everything about statistics and biology. This is not a good text for any one just starting out and I highly doubt it would be of much value to someone who is already in the field of Bioinformatics.

Not worth it at half the price.
I bought this book for the Data Analysis, Modeling, and Visualization class run by the author, Arun Jagota. It is little more then the notes he uses in his class bound in a nice little book. There are very few meaningful examples and the text assumes the reader already has a knowledge base in the subject since little detail is given in most areas of the book. If you are going to take this class and have to buy the book then I would suggest saving your money and buying the pdf file version of it and printing it out. Chances are you won't use this text very often.

Not good for students and beginners
Despite of an appearance of being mathematically sound, Jagota's book is full of hand-waving and imprecision. The author jumps from one topic to another using unexplained terminology (particularly in examples.) I also don't trust the author's attitude of commenting on the well established statistical terminology in a way that can confuse and mislead the reader. One example of such misleading comments is implying the existence of "symbolic random variable." In fact the "symbolic random variable" IS a "usual" random variable, as it should. But it is a minor problem comparing to the overall lack of precision, missing background explanations and often inadequate references.

In my judgement the book could be used by experienced bioinformaticians as a non-essential desk reference. Beginners and serious students should keep far away from it. ... Read more

Book Description

This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis. ... Read more

Book Description

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 importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms: Curation and delivery of biological metadata for use in statistical modeling and interpretation Statistical analysis of high-throughput data, including machine learning and visualization Modeling and visualization of graphs and networks The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. 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. ... Read more

Reviews (1)

An innovative look at biotech...
This is an exciting book - it's basically a look at biotech from the angle of new media or media studies. Clear, astute descriptions of current research, as well as innovative ways of looking at the research. And best of all, there's none of the familiar jargon of cyborgs, cyberspace, or "virtual" this or that. Thacker goes beyond what alot of science studies and media studies have said on this topic, and opens up worthwhile new avenues to explore.

As a recent grad student, I've read more than enough books on biotech. Also, I've enjoyed Thacker's various posts to lists like Nettime and other online journals; he always seems to cut to the core of an issue, but without simplifying things. An example is the chapter on systems biology, which raises alot of basic questions for research today. Thacker looks at a range of approaches that eschew the gene-based imperatives of the drug R&D industry, but also shows their differences. For instance, the tendency now towards systems biology often doesn't question the basic gene-centrism of alot of research. Complexity and other approaches do offer alternatives, but ones that exist separate from the imperatives of the drug R&D approaches.

The chapters on bioinformatic and DNA computing are also really good, as it patiently sorts out the tensions between materiality and immateriality. I appreciated this attention to philosophical issues, while remaining rooted in the technology itself. Perhaps the biggest lesson one can take away from this book is Thacker's concept of "biomedia": that biology and biotech is integrated with information technology, but that does _not_ mean that biology is becoming immaterial - it means the opposite.

On a final note, I'm not a big science fiction fan, so the segments on sci-fi wasn't as effective for me, but the juxtaposition of science and sci-fi is interesting. I'd recommend this alongside other like-minded work, such as Richard Doyle's Wetwares. ... Read more