Book contents
- Frontmatter
- Contents
- Foreword by Steven Salzberg
- Preface
- Acknowledgements
- 1 Introduction
- 2 Mathematical preliminaries
- 3 Overview of computational gene prediction
- 4 Gene finder evaluation
- 5 A toy exon finder
- 6 Hidden Markov models
- 7 Signal and content sensors
- 8 Generalized hidden Markov models
- 9 Comparative gene finding
- 10 Machine-learning methods
- 11 Tips and tricks
- 12 Advanced topics
- Appendix
- References
- Index
Preface
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Foreword by Steven Salzberg
- Preface
- Acknowledgements
- 1 Introduction
- 2 Mathematical preliminaries
- 3 Overview of computational gene prediction
- 4 Gene finder evaluation
- 5 A toy exon finder
- 6 Hidden Markov models
- 7 Signal and content sensors
- 8 Generalized hidden Markov models
- 9 Comparative gene finding
- 10 Machine-learning methods
- 11 Tips and tricks
- 12 Advanced topics
- Appendix
- References
- Index
Summary
This book grew out of a number of conversations between Dr. Ian Korf and myself, in May of 2004, in which we jointly lamented the rather large number of small but important details which one is required to know when building a practical gene-finding system from scratch, and which were at that time not fully documented in the gene-finding literature. Page limits in traditional print journals invariably force authors of research reports to omit details, and although online journals have relaxed this constraint to some degree, the larger impact factors of the more venerable print journals still attract many researchers to those more constrained venues. Of course, even the online journals urge authors to be brief, so that implementation details are either relegated to supplementary online documents, or – more often than not – simply omitted entirely. Because practical gene-finding software typically consists of many thousands of lines of source code, including all the details necessary for replicating any of these programs is out of the question for any journal article. Placing one's gene finder into the public domain, or making it open source, can help to alleviate this problem, since the public is then free to peruse the source code. Of course, perusing a 30 000-line program can be tedious, to say the least, and ideally one would like a format more geared toward human consumption, in which the essential concepts have been distilled from the implementation and provided in a rigorous but accessible manner.
- Type
- Chapter
- Information
- Methods for Computational Gene Prediction , pp. xiii - xvPublisher: Cambridge University PressPrint publication year: 2007