Sequence Comparison - Theory and Methods

Foreword My first thought when I saw a preliminary version of this book was: Too bad there was nothing like this book when I really needed it. Around 20 years ago, I decided it was time to change my research directions. After exploring a number of possibilities, I decided that the area of overlap between molecular biology and computer science (which later came to be called " bioinfor-matics ") was my best bet for an exciting career. The next decision was to select a specific class of problems to work on, and the main criterion for me was that algo-rithmic methods would be the main key to success. I decided to work on sequence analysis. A book like this could have, so to speak, straightened my learning curve. It is amazing to me that those two conclusions still apply: bioinformatics is a tremendously vibrant and rewarding field to be in, and sequence comparison is (arguably , at least) the subfield of bioinformatics where algorithmic techniques play the largest role in achieving success. The importance of sequence-analysis methods in bioinformatics can be measured objectively, simply by looking at the numbers of citations in the scientific literature for papers that describe successful developments ; a high percentage of the most heavily cited scientific publications in the past 30 years are from this new field. Continued growth and importance of sequence analysis is guaranteed by the explosive development of new technologies for generating sequence data, where the cost has dropped 1000-fold in the past few years, and this fantastic decrease in cost means that sequencing and sequence analysis are taking over jobs that were previously handled another way. Careful study of this book will be valuable for a wide range of readers, from students wanting to enter the field of bioinformatics, to experienced users of bioin-formatic tools wanting to use tool options more intelligently, to bioinformatic specialists looking for the killer algorithm that will yield the next tool to sweep the field. I predict that you will need more that just mastery of this material to reach stardom in bioinformatics – there is also a huge amount of biology to be learned, together with a regular investment of time to keep up with the latest in data-generation technology and its applications. However, the material herein will remain useful for years, as new sequencing technologies and biological applications come and go. vii viii Foreword …