Introduction to Computational Genomics: A Case Studies Approach (英語) ペーパーバック – 2006/12/14
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Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.
Nello Cristianini is a Professor of Artificial Intelligence, University of Bristol.
Perhaps the next version will be better.
The book opens up with a quick review of the relevant aspects of cellular biology and statistics. This might be enough for readers with no knowledge of biology, but I think it's better used as a review. If, for example, you don't know what a nucleotide is or what transcription is, I think you might want to learn that material somewhere else before reading this book. However, others may disagree.
The topics discussed were relevant and interesting. They include gene finding, sequence alignment, Hidden Markov Models (my first exposure to this topic), some applications to evolutions such as phylogeny, whole genome screening, regulatory sequences and gene expression. I found the quality to be uniformly very good. Many calculations were done in detail.
Most of the book deals with basic principles, but as the title implies it uses specific case studies to illustrate the theory. In addition to providing examples of how to apply the theory, the case studies were interesting in their own right. A couple of my favorites were calculating the genetic distance between Neanderthal and modern humans and how gene expression is important in wine making.
While the emphasis is on learning the fundamental concepts a few tools/resources were briefly mentioned including BLAST, FASTA format, GenBank, PAM and BLOSUM. However the coverage of these is minimal. If you're looking for a book on existing bioinformatics tools like BLAST then this book probably wouldn't be a good choice (for that I thought "Bioinformatics, A Practical Guide to the Analysis of Genes and Proteins" by Baxevanis and Ouellette, was pretty good)
One kind of odd thing (odd in my experience anyway) is that when describing matrices a (column, row) notation was used, for example on page 43 a matrix with 2 rows and c columns was described as a cx2 matrix.
I think this book provided a very good introduction to a fairly wide variety of concepts in bioinformatics. If you have studied these topics previously this book might be fun to read, but you probably wouldn't learn much (except perhaps from the case studies).
This book is a great introduction for nonbiologist and is of reasonable length (less than 200 pages). A newbie should be able to learn the basics from this book. Each chapter provides a reading list, which includes both historically important references and references that are still of current interests.
The cases that are included in this book are relatively new and are still related to research frontiers in this field.