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| By: Mahmoud H. Hamdan, Pier G. Righetti ISBN: 0471648175 Publisher: Wiley-Interscience Release Date: 11 February, 2005 Bioscience book rank: 170491 | ||
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| By: David B. Allison, Grier P. Page, T. Mark Beasley, Jode W. Edwards ISBN: 0824754611 Publisher: Chapman & Hall/CRC Release Date: 14 November, 2005 Bioscience book rank: 854752 | An expansive tour of existing statistical methodologies used in microarray data analysis from contributed experts. What it does is deeply explore what people are using nowadays, cites references in each chapter beautifully, and gives the reader a solid footing as to the pros and cons of each technique presented. Each chapter delves deep enough, and by citing references extensively, one can go seek further detail from the source. <br /> <br />IMHO, this is probably one of the most professionally done books out there. By that I mean there is no fluff, just high quality content across the board. The intended audience is those having some computational background, particularly in statistics. However, each chapter begins with an introductory paragraph serving as a review so that anyone could pretty much gather what is going to be discussed. <br /> <br />A really nice feature of this compilation is that not only does it provide insight into the current methods, it outlines where further developments are needed and how they may help in filling in the gaps. With so many developments having been made in microarray data analysis recently, its nice to know that this book exists to help one gain a solid foundation on how to proceed with some sort of clarity. A five star rating for this one hands down. :) | |
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| By: Geoffrey J. McLachlan, Kim-Anh Do, Christophe Ambroise ISBN: 0471226165 Publisher: Wiley-Interscience Release Date: 04 August, 2004 Bioscience book rank: 973349 | ||
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| By: Eric M. Blalock ISBN: 1402074727 Publisher: Springer Release Date: 01 July, 2003 Bioscience book rank: 657608 | ||
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| By: Phillip Stafford ISBN: 1420052780 Publisher: CRC Release Date: 31 January, 2008 Bioscience book rank: 1174319 | ||
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| By: Mark Schena ISBN: 0763731277 Publisher: Jones and Bartlett Publishers, Inc. Release Date: July, 2004 Bioscience book rank: 999012 | ||
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| By: Isaac S. Kohane, Alvin Kho, Atul J. Butte ISBN: 0262612100 Publisher: The MIT Press Release Date: 01 September, 2005 Bioscience book rank: 490926 | The authors of this book are very excited about the prospects of the field of functional genomics and DNA microarray technology. Their optimism however is tempered by a large degree of caution, for they make it clear in the first few paragraphs of the book that expression profiling using microarrays is still in its infancy and that there have been exaggerated reports of its success. They wrote this book with the intent of giving the reader a more realistic view of microarray technology and have succeeded in their goal. They target the book specifically to experienced biologists and bioinformaticians with limited experience in using microarrays, and to students who are entering the field of bioinformatics. Most importantly, they emphasize that functional genomics is an experimental science, and that highly sophisticated algorithms from data mining or other areas of artificial intelligence will be of no assistance if the experimental information is not there in the first place. They do encourage however further development of these algorithms, in order to be able to extract the data as it becomes available, and as microarray technology itself matures. Even with the current technology, enormous amounts of data are generated, and if sense is to be made of this data, one will have to develop more effective algorithms than what are currently available.
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<br />To perform successful experiments, the authors describe a `functional genomics pipeline', and list the characteristics that it must have, consisting of both `wet' (laboratory) and `dry' (computational) steps. They devote a lot of space in the book describing how to develop an effective genomic experiment. Crucial to such investigations they say is a design that maximizes the possibility of observing relevant gene expression patterns, and the `experiment design space', which encapsulates all possible conditions that a particular biological system could be influenced by. Also important to the design is the `expression space', which is the collection of all potential expression values of all genes in a given genome. One could view the expression space as a vector space of high dimension, with each dimension corresponding to a single gene. Of great interest, and widely discussed in the general bioinformatics literature under the guise of the new field of `systems biology' is a subset of the expression space called the `transcriptome.' This subset models the expression of a cellular system under all stimuli. Considering that one might have to deal with 30,000 genes in the case of a human, the characterization of the transcriptome will be a formidable project. Interactions between the genes will complicate the analysis even further. The authors view each experiment as being an exploration of the space of all possible expression patterns, and describe good experimentation as being the `maximal exercise' of the genome. This consists of finding those correlations between the genes that have the greatest impact on the process under scrutiny.
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<br />A book on microarrays would not be complete if it did not discuss how they actually function. This is done in a fair detail in chapter three of the book. The authors do not favor a particular vendor but rather discuss what biological assumptions all microarray technology is based on. One of these assumptions is, as expected, that there is a direct connection between mRNA transcription and the protein translation associated with it.
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<br />In any laboratory experiment one has to deal with experimental uncertainty or "noise." This involves the influence of unknown external perturbations that result in variability in the outcomes of the experiment. As further evidence that the authors are careful experimenters, they discuss noise in detail, noting first that expression experiments deal with information that is both digital (DNA sequence information) and analog (mRNA expression levels). They distinguish between `intra-chip' noise, which arises when one probe feature influences another, improper scanning techniques, and manufacturing defects, and `inter-chip' noise, which arises from sample variation. Normalization issues are also discussed. Readers should take particular attention to the discussion on fold calculation and significance because of its connection with statistical analysis and because it sets the tone for the rest of the book. In particular, this discussion leads to the very important topic of dissimilarity and similarity measures. This part of the book is more sophisticated mathematically than what has been encountered so far, dealing for example with the concept of a metric space, which may appear to be somewhat abstract by readers who are not mathematically astute. Linear correlation and mutual information are two examples of metrics that are discussed.
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<br />Data mining is of course heavily discussed in the book, along with the new field of `ontological engineering' and how the latter is used functional genomics. Data mining is of course a vast field, but the authors give the reader a good taste of how some of its techniques can be applied to analyze microarray experiments. Both unsupervised and supervised learning is discussed, along with `self-organizing maps.' The authors end the book with their vision of future developments. Naturally they point to further refinements in microarray technology, the need for educating a new generation of bioinformaticists, and the push towards the development of new data mining algorithms. Certainly all of these are important, and one can expect other technological developments to occur in the coming years that may prove superior to microarrays in their application to functional genomics. In addition, and there are indications of this even at the present time, one can expect technologies that fully automate the study of gene expression. This includes the generation of hypotheses that characterize scientific investigation, the development and construction of the experiments themselves, and the analysis of the resulting data. 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. 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. | |
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| By: Shailaja R. Deshmukh, Sudha G. Purohit ISBN: 1842654233 Publisher: Alpha Science Intl Ltd Release Date: 30 August, 2007 Bioscience book rank: 651077 | ||
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| By: Mark Schena, Steen Knudsen ISBN: 0471678538 Publisher: Wiley-Liss Release Date: 29 April, 2004 Bioscience book rank: 1321759 | ||
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| By: Alan R. Kimmel, Brian Oliver ISBN: 0121828166 Publisher: Academic Press Release Date: 28 August, 2006 Bioscience book rank: 1265300 | ||
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methods in molecular biology, PCR, RT-PCR and real-time quantitative PCR, Differential Display, recombinant DNA, gene therapy, virus protocols, lentivirus methods, gene targeting, mouse knock-out and knock-in, transgenic technology, phenotyping, gene delivery and transfer, transcriptional regulation, RNA methods, RNA Polymerase, gene expression, protein translation regulation, protein kinase, protein phosphorylation, genomics, genomics methods, epigenetics, DNA methylation, DNA sequencing, RNA interference, microarray
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