Computational Genome Analysis: An Introduction (Statistics for Biology & Health S) by Richard C. Deonier (PDF)

Description

This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This textbook is used as the main text for one of my graduate courses. It is a well written book and contains a plethora of information. The problem is that I find myself constantly re-reading sections and walking through examples to thoroughly understand them. Nothing seems to click the first time I read through the information (or sometimes even second, third, etc.).This is my first time taking any coursework in the bioinformatics field so perhaps it is simply because this material is new to me, but I found this book fairly difficult to read. I had to supplement it with other books, wikipedia entries, etc. to be able to understand many of the terms (which this book fails to define).If you’re willing to put forth the effort of filling in the gaps, then this is a great book. If you already have a strong background in computer science and biology then this is likely an excellent book for reference material, or to expand you knowledge in an already familiar area.Also note that there is a large amount of discussion of probability in this area of study. You may wish to brush up on your skills in probability prior to reading this.

⭐Dears:The book is really a good intro to the subject. This was my first book on the subject and I think I did the right choice and ended up with a very good feeling on what means the application of computers and statistics on genome stuff. But I think the book’s title rather be “Statistical Genome Analysis,” due to the fact that the authors give more strength on the statistics techniques used when analyzing genome data, what is cool. “Computational” is tied to some R codes, shown throughout the book, actually, very good hints on using R to do some basic stuff with genome data. Of course, due to the date of publication of the book (2005) many web links are outdated or doesn’t exist any more. But nothing that a Google search couldn’t solve it. And, of course, due to the accelerated advance of the technology in the field of genomics, like sequencing, some concepts are outdated, too. I have heard from some bioinformatics PhD that microarray tech, for example, is with its days numbered, entering RNA-seq.Of course, this doesn’t take the merits of the book. If you, reader of this note, is interested in buying the book, go on and do it! You, like myself, will not be disappointed.

⭐I just got this book as a Kindle ebook. I don’t know about the print version, but on the Kindle the font used makes it very difficult to read, as the letters are incomplete to the point where an “r” looks like an “i” for example. Trying to read it I kept stumbling over the text and my attention was on deciphering the letters, so I could not focus on the actual contents. I expect this to be an even bigger problem when it comes to the technical portions, where precision is important. I would not recommend getting this on the Kindle, or at least get a sample first to see if you can read it.

⭐This textbook was based on the authors’ instructional experiences in undergraduate Computational Biology courses for Bachelor seniors, first-year Master’s, and Ph.D. students at the University of Southern California. Readers could also include investigators in medical schools, computer scientists, biologists, applied mathematicians, biochemists, and persons working in the biotechnology industry.This text is based on the classic man-machine-work model in which a human performs laboratory-level work while also interacting with a digital computer. The complete inventory of all DNA that determines the identity of an organism is known as the genome. The computer or ‘machine’ utilizes the R language and produces statistical solutions dealing with genomes. The objects analyzed fall into these categories: the basic unit of life or the cell; the chemical energy stored in ATP (Adenosine triphosphate), the genetic information encoded by DNA (Deoxyribonucleic Acid) , and that information transcribed into RNA (Ribonucleic Acid). Since all life on the planet is based on cells, except for viruses, one can see why this volume is an important contribution to the scientific knowledge base particularly with reference to the evolution of species.The R language developed at Bell Laboratories is used throughout the text. R is a probability statistics environment available for free download and can be used with Windows, Macintosh, and Linux operating systems. It functions very much like the S-PLUS statistics package. Since the reader would need to know how to actually implement the concepts in computa­tional biology to fully understand them, the authors include examples of computations using R. This volume is described as a “roll up your sleeves and get dirty” introduction to the computational side of genomics and bioinformatics. It is intended to provide a foundation for an intelligent application of the available computational tools and for in­tellectual growth as new experimental approaches lead to new computational tools.One must accept the fact that analyzing cells, DNA, and RNA is based on probability statistics. The text utilizes 1% algebra, 1 % integral calculus and 98% probability statistics — the 98% being processed in R language. It isn’t intended to describe the laboratory processes and protocols used to manipulate the samples but it does directly connect the computer solutions to the laboratory or work activity. Each chapter ends with a number of problems; while this is typical of the classical textbook, it would have been helpful if a teacher’s answer book had been appended.The Chapter headings are: Biology in a Nutshell; Words, Word Distributions and Occurences; Physical Mapping of DNA; Genome Rearrangements; Sequence Alignment; Rapid Alignment Methods: FASTA and BLAST; DNA Sequence Assembly; Signals in DNA; Similarity, Distance, and Clustering; Measuring Expression of Genome Information; Inferring the Past: Phylogenetic Trees; Genetic Variation in Populations; Comparative Geonomics; Glossary; A Brief Introduction to R; Internet Bioinformatics Resources; Miscellaneous Data.Leonard C. SilvernSystems Engineering LaboratoriesClarkdale, AZ

⭐The book was delivered very fast (from the US to Canada)! The book is old and many links are not working. But it has very well covered the genomic analysis through a statistical approch which was what I wanted as a statistician. I gave it four stars since it is an old text and lacks the new advances in bioinformatics.

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