An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.
The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively.
An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.
PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Author’s website.
About the Author(s)
Neil C. Jones is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of California, San Diego. Pavel Pevzner is Ronald R. Taylor Professor of Computer Science at the University of California, San Diego. He is the author of Computational Molecular Biology: An Algorithmic Approach (MIT Press, 2000) .
Excellent algorithms exercise & bioinformatics intro, September 24, 2005
This is the first book that I’ve read regarding bioinformatics, so Im updating this as my class moves along. You better have a grasp of basic data structures prior to beginning this book and background with a programming language as there is very little hand-holding in this text. A bio background makes it all more interesting but certainly is not critical. There are no sample code or sources printed with the book nor is there an included CD nor answers to exercises. There is an associated web site where some ideas may be had and errata found/reported, but its not very active that I have seen. The pseudo code in the book is very python-like so easy to make use of. I personally transfer the book’s concepts to C/C++ (habit) without much problem, except sometimes my results differ from the book. Apparently these are book bugs, so be sure to check the web site out if unexpected things pop up.
Presently my class is in chapter 8 (of 12) and looking back I would like to caution that some data processing algorithms will drive a computer’s CPU quite hard so be aware of battery-munching & heat. My only bones with this book so far are the alphabet soup of variables and lack of answers to exercises. It would be nice if variable definitions were refreshed at the beginning of pseudo code samples.
I like this book as an algorithms text over traditional texts because the applications are much more fascinating. Imagine searching for something and you don’t know where that something is. On top of that add not even knowing exactly what it is you are looking for. And when you do find it, its not even in the data searched! This may sound unlikely or even impossible, but it is neither. Rather, its very cool.