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Buy Bioinformatics in the Post-Genomic Era: Genome, Transcriptome, Proteome, and Information-Based Medicine by Jeff Augen available and for sale on Brightsurf
| View Larger Image | Bioinformatics in the Post-Genomic Era: Genome, Transcriptome, Proteome, and Information-Based Medicine
by Jeff Augen
| | List Price: | $44.99 |  | | Available: | Usually ships in 24 hours | | | | | Sales Rank: | 1138976 | | Studio: | Addison-Wesley Professional | | | Binding: | Paperback | | Number Of Pages: | 408 | | Publication Date: | September 06, 2004 | | Publisher: | Addison-Wesley Professional |
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CUSTOMER REVIEWS (Average Customer Rating: 3.0 based on 1 review)
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decent intro, but not comprehensive or very usable 
Bioinformatics is the science of biological information, namely sequences and metadata about organisms and sequences. What's interesting about this field to many people, both in the sciences and outside of it, is the large volume of data that gets analyzed and the results that emerge on a daily basis. Obviously interesting for the medical advances and the rapidly growing business in the life sciences, there's a complex field that has developed in the past ten years or so. And following the sequencing of the human genome, new challenges have arisen for everyone involved. Augen's Bioinformatics provides a good introduction to this new field of research for students in the sciences, and anyone with a decent undergraduate education in modern biology. I think that this accessibility of the material is one of the book's biggest winning points.
After an introduction to the book and the subject area of bioinformatics (chapters 1 and 2), Augen begins at the level of the structure of a gene (chapter 3). Here, anyone with an undergraduate level understanding of genetics or molecular biology can begin using the book and bridging the gap to the new areas of modern bioinformatics. Augen then describes how basic sequence analysis is performed at the DNA sequence level (in chapter 4). The material in Bioinformatics covers some of the higher-level methods for sequence analysis, including hidden Markov models, neural networks, and pattern discovery, and introduces some of the common algorithms found to do this analysis.
Chapter 5 then covers transcription, the process of going from DNA to mRNA. Beginning with the biology behind this activity (the ribosome and the larger "transcriptome"), Bioinformatics then describes how you would perform transcriptional analysis. Here, Augen shows how you go from a wet lab to a computational lab and describes what classes of experiments you perform to gather data and then what kinds of analysis you perform on it. This chapter introduces some of the more common clustering techniques for data aggregation and understanding.
The next step in the DNA -> RNA -> protein chain is found in chapter 6, which covers the translation process. Coupled to chapter 7, which describes protein structure prediction and searching, these two chapters bridge the next gap between laboratory data and computational analysis. Protein folding and structure analysis was one of my pet areas of study as a graduate student, and Augen's text does a decent summarization of the field to date. The resources listed and techniques described are definitely on par with the common practices in the field.
Finally, Bioinformatics gets into the next major area of bioinformatics, medical databases. Augen's bridge from genetics to medical science is complete, and he discusses how medical professionals utilize databases and can begin to predict disease, for example, based on data mining. The final chapter, "New Themes in Bioinformatics," covers exactly that, but also what Augen refers to as "workflow computing," or basically going about being a bioinformatics scientist. One of my favorite emerging areas in bioinformatics, metabolic pathway elucidation, is also covered briefly.
I've shared this book with a few friends who are all studying computer science or practicing computer scientists. I did so because Augen's material does a good job of explaining my background and introducing them to some of the analysis forms I introduce into my own work. It does a good job of that, and gets them quite excited. Bioinformatics really bridges a number of fascinating areas of computer sciences, including data mining and high performance algorithms. Augen's Bioinformatics is a good introduction to the field for them, and really anyone who has studied a couple of biology courses in college.
Where the book falls short, however, can be grouped into two main areas. The first is the failure of Augen's presentation of the algorithms. While the methods used to describe computational algorithms in Bioinformatics is common for non-computer scientists, it's completely unusable for computer scientists who are used to a specific algorithm presentation style that looks more like pseudocode than rambling text. The ambiguities this presents for a technical reader are unfortunate, especially if anyone studying bioinformatics is supposed to be computer science literate. The book itself assumes a life science literacy, so this isn't an unreasonable expectation of the reader.
The second area that consistently falls short in the book is in the utility of the information given. While I am significantly happier with the quality and depth of material presented in Augen's book than in the O'Reilly bioinformatics series, where the book fails to deliver is in showing the reader how to actually use the data they gather. After all, the book shows various sequence analysis algorithms and discusses tools available to do this work, but it only devotes a few pages (out of over 370 in total) to a workflow that can be used. Also, the book fails to point the reader at very worthwhile web resources sometimes, including meta sites like the SDSC Biology Workbench site, and just says "some Perl scripts" for local data analysis. As such, you'll have to go a few extra miles on your own to make use of the data sources.
I guess a third complaint of the book for me is that Augen has ignored or omitted significant bodies of research that fit squarely into the scope of the book. For example, Ken Dill's research into protein folding models, as well as Martin Karplus' work on the subject, receives no mention, nor does the topic of Bayesian network analysis when Augen discusses time series data analysis. These aren't new, they've been around for many years and influenced most of the field, and their absence is noted. The book's spotty coverage in some places, like these, is noticeable.
Bioinformatics does a few things well, but overall reads too much like a biology textbook to be useful to the average computer scientist. More emphasis on the practice of bioinformatics and data analysis would have made this book stronger and complemented the substantive background material well. Finally, using an approach more similar to the computer science approach would have been a tremendous benefit, since the material really is computer science in part. That said, I think this is probably the best introduction to this exciting area of science that I have yet seen.
April 06, 2005 | |
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