Customer Reviews

Bioinformatics: Sequence and Genome Analysis, Second Edition (Mount, Bioinformatics)

5 star:		(5)
4 star:		(4)
3 star:		(1)
2 star:		(5)
1 star:		(5)

 

 

The field of bioinformatics has exploded in the last five years, and several monographs and textbooks have appeared to assist in the elucidation of the concepts involved. Bioinformatics is a field that grew hand-in-hand with the rise of the Internet, and anyone going into it will need expertise in the PERL and JAVA programming languages, as well as a fairly strong mathematical background. In this book, the author gives a very good overview of bioinformatics from mostly a qualitative and descriptive point of view, although some elementary mathematical discussions are inserted in various places. Because of the level of mathematics used, this might not be the book to use for the mathematician who desires to go into bioinformatics or computational biology. On the other hand, for the student of biology or mathematics who intends to pursue bioinformatics as a profession, this book would be an excellent choice. One cannot read the book however without visiting its accompanying Website, for the author extends some of the results of the book there.

The book begins with an historical introduction to the subject, and a newcomer to the subject will get a brief overview of some of the first sequence analysis programs and some of the first DNA sequence databases developed long before bioinformatics was recognized as a real discipline. The author introduces some of the techniques that will be discussed in the book, such as global and local sequence alignment, dynamic programming, RNA structure prediction, and protein structure prediction. This is followed in chapter 2 by an overview of the procedures used to collect and store sequences in the laboratory. To the reader not familiar with these techniques, the discussion may be too brief. The different sequence formats used are outlined, as well as techniques used to convert from one sequence format to another.

Chapter 3 takes a closer look at the pairwise alignment of sequences, and the author also outlines the reasons behind examining sequence alignment in the first place, namely that of finding the functional, structural, and phylogenetic information. The view of sequence alignment as an optimization problem is emphasized via the dynamic programming algorithm for sequence alignment. Dot matrix analysis is discussed a sequence alignment strategy that allows all possible matches of residues between two sequences. The author is careful to note that local alignment algorithms might give global alignments, and vice versa, because of small changes in the scoring system. The PAM and BLOSUM substitution matrices are compared as to their relative merits and pitfalls. A very detailed discussion of gap penalties is given, along with the role of the Gumbel extreme value distribution in the determination of the statistical significance of a local alignment score between two sequences. And, after a brief introduction to Bayesian statistics, the author shows how to to use it produce alignments between pairs of sequences and to calculate distances between sequences. The Bayes block aligner software package is discussed in detail as a tool for Bayesian sequence alignment.

In chapter 4, the author gives an extensive discussion of multiple sequence alignment algorithms, the most important of these by contemporary standards being hidden Markov models. The author though does treat the "progressive" methods, as well as the use of genetic algorithms in doing multiple sequence alignment. The former include the classic CLUSTALW package and the PILEUP program for doing msa. Although the discussion of hidden Markov models makes sparing use of mathematics, is does serve to explain how they work and should assist readers who need a solid understanding of them.

I did not read chapters 5 and 6 so I will omit their review. Chapter 7 is an introduction to database searches in order to find similar sequences. The algorithms developed in chapters 3 and 4 again make their appearance, and the reader is confronted with various user interfaces for performing genetic database searching online. The FASTA and BLAST tools are introduced as fast methods to do database searching. As computer performance increases in the years ahead, these and other currently existing tools will no doubt be replaced by more powerful search routines. While perusing this chapter, one cannot help but be fascinated by the current situation in the biological/genetic sciences. Once thought of as a purely descriptive science, it is now dominated by a reductionist philosophy, involving huge amounts of data, and sophisticated mathematics for the analysis of this data.

The author moves on to the methods for detecting protein-encoding regions of DNA sequences in chapter 8. The simplest method according to the author for doing this is to search for ORFs, and he discusses the reliability of methods for accomplishing this. Hidden Markov models again make their appearance as a tool to study eukaryotic internal exons and in gene prediction in microbial genomes. And, neural networks are introduced as tools for finding complex patterns and relationships among sequence positions, and Grail II is discussed as a system for exon finding in eukaryotic genes. Promotor prediction in E. Coli is also briefly overviewed.

I did not read chapter 9 so I will omit its review. Chapter 10 though is an introduction to one of most interesting parts of bioinformatics, namely that of analyzing the entire genomes of organisms. Due to rapid experimental advances in genetics, several genomes are now available, and this allows a more global, dynamical view of the role of genes and how their expression correlates to result in a fully-developed functioning organism. The techniques discussed in earlier chapters come into play in genomic analysis, and many other more novel techniques will have to be invented if sense is to be made of the enormous amount of genomic data currently available.

This book has been heavily hyped but the publisher should have put more work into the editing. There is much information here -- a comprehensive set of URL's make the book worthwhile. However I found the book difficult to read -- after analyzing why I realized the author didn't believe in the one concept per paragraph style of writing. Instead he repeatedly attempts to cram too many ideas into too few paragraphs making the book a slow slog.
Bioinformatics is hot now and there is a rush to publish -- this book could have used another few months of polishing before going to press.

I am an undergraduate Biotechnology student who is using this book for an intro Bioinformatics class at the Junior/Senior level. It describes the field from a biologists perspective, and doesn't include too much math. It describes the steps that an algorithm within the program would use, and the logic behind them, without going into the complexities of the coding.

While this is a book by and for Biologists, I have found the book to be very rough and in need of extensive editing. On the first test the professor was disappointed as only 1-2 people made it into the A range. At the time I wondered if it was related to the difficulty of the text. To my surprise, my professor began to give us 10 to 20 page handouts per class, covering the material in his lectures. Although he never directly stated this, the handouts were apparently there to make up for the weakness of the text.

It definitely has potential to be a good text for biologists. If the author and editor put in significant work, this could really become a very good book. However I really can't recommend the current edition.

I don't understand such a lot of bad comments about this book. In this book concepts are presented in an intelligent way, because the book is as quantitative as the biologist's requirements are. Everithing is sufficient to comprehend which are the things' mathematical basis but avoiding time-comsuming and ready-to-forget extra info. Other books are only for matematicians because they are sometimes full of numbers and complicated equations, while other ones are so simple that I imagine them usefull only for non-biologists (matematicians again above all).
This is a book that is usefull as an introduction for the initial graduate level bioinformatician (biologist) and as a short description of the techniques that we use to matematicians aimed to collaborate.

Finally I am not in agreement with some coments about what is Bioinformatics. Most of them carried out by some non-biologists here. Bioinformatics is Biology. Of course we use mathematics, but as far as we USE them, bioinformatics is not mathematics. We do not develope Mathematics, but Biology state of the art. Bioinspired algorithms, in the other hand, are pure mathematical concepts, even if they are insipred in biology. Let Bioinformatics be what it is, a quantitative and statistical part of pure Biology.

This is a good book if you are not an expert in Bioinformatics but you have in mind to be one. Study this book entirely as your first one and go directly to the difficult ones. For me, it is the shorter reading path to bioinformatics expertise nowadays.

If you are a biologist and just want to know some background information of how to apply bioinformatics to your research, do not read this book. My recommendation for you is "developing bioinformatics computer skills" and some other books like that.
If you are a student or scientist who study bioinformatics, this book is an excellent book and really worthy to read. This books gives very detailed information on algorithm to help us understand how the software such as BLAST and FASTA are designed. The illustrations are easy to understand compared with other books I have read, especially for the statistics part of any algorithm.
One weak point is that the book focus on nucleic acid sequence analysis while talk little about protein.

This is the required book for our graduate-level computational biology class. I think I represent most of the class when I say the book does a very poor job explaining concepts. There seems to be a pervasive fear of mathematics, which leads to long confusing attempts to explain algorithms by words alone. The writing also seems unnecessarily wordy and opaque. This book also contains many typos, though that may improve in future editions. For some reason, it also costs at least twice as much as most other books on the topic.

This is really the best bioinformatics book I have encountered. My bias is that it seems that most of these books are written by computer scientists for computer scientists. This book is written by a biologist for biologists, and focuses on solving biological problems from the point of view of which tools are good for which jobs. This is an excellent introduction into all sorts of tools that you may want to use, from simple BLAST searches to more complex types of evolutionary trees and the like. As a biologist, I have found this book to be a lot of help, and I wish that I had encountered this book a few years ago. Computer scientists may want more mathematical details, and should look to other books. But as a guide for a biologist, this book can't be beat (and I've looked through a dozen or more other bioinformatics books). As you can see, I can't say enough about this book. If you're a biologist, get this book, and you'll see.

While this book may, and I stress the word may, contain useful information, it is so badly written that it is incomprehensible. Dr. Mount seems to believe that ten words are better than one, making "Bioinformatics" very tedious to read. After awhile I felt like I was reading a Victorian novel.
Despite being wordy, the explanations are too brief and not clear. If you don't know what he is talking about before hand, you will never understand what he is explaining. He uses an excess of words, and rarely provides a clear, concise example of what he is referring to (or if he does it is in another chapter in the book).
It also appears that the book was never edited. For example, when trying to define "ortholog" and "homolog," he writes two opposing definitions for ortholog and none for homolog. Clearly this is a mistake and Dr. Mount accidentally used the word ortholog twice while meaning to use ortholog once and homolog the other time (pg 56). While it can be argued that this mistake is unimportant and the reader can look up the definitions, it makes me wonder what else in the book is wrong that I have no way of detecting (until I waste a bunch of time doing something incorrectly).

I used this book in an introduction level class to bioinformatics and it was worse then useless. The book is much more a survey of literature then anything else and so if you are not already very familiar with the topics the book does not provide enough details for you to get very far at all. Although to be fair most of the books on bioinformatics out at the time and the two years after were not much better, but I felt this was near the bottom of the pile. "Fundamental Concepts of Bioinformatics", ISBN: 0-8053-4633-3 when it came out was miles better, although even that book had tons of warts. If you are looking for a reference then this book is okay, but by the time I am writing this review you assuredly can find a more modern book.

Part of the problem with books on bioinformatics is that, every book makes very different assumptions on your base level of knowledge of the various critical subjects needed: biology, chemistry, computer science and math. Most strike a pretty poor balance on the assumptions made and vary from way too basic to useless to anyone who is not already familiar with the field. My suggestion is to check out any book you are considering because how good the book is will vary greatly on your background.

This book will give you very strong foundations in
the basics of computation in the bio world. Though
this book does not give details of the computation
methods, it does give a very clear picture of math-
ematics and the science involved.

This book has a good coverage of FASTA and
BLAST. (Though a little bit short)

The programming techniques coverd are bare. Though
concepts like searching sequences using dynamic p-
rogramming are covered, you are better off reading
something like Proteome Research by wilkins et al.

I am yet to find a good book that deals only with
the technical and programming aspects of bio informatics
if you do find some thing interesting lemme know.

On the whole this book helped me understand a lot
about sequencing, alignment and prediction. The illustrations
and pictures provided are good and the text to the point.

If you are reading this review pls understand that I am
primarily a programmer trying to get into the
bio informatics business. I do not have any schooling
or degree or even experience in the bio informatics world.

Hope this helps

Santy