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About this book
About this book
Computational biology is a rapidly expanding field, and the number and variety of computational methods used for DNA and protein sequence analysis is growing every day. These algorithms are extremely valuable to biotechnology companies and to researchers and teachers in universities who don't have time to find the best algorhythms. This book explains the latest computer technology for analyzing DNA, RNA, and protein sequences. Clear and easy to follow, designed specifically for the non-computer scientist, it should help biologists make better choices on which algorithm to use. New techniques and demonstrations are elucidated, as are state-of-the-art problems, and more advanced material on the latest algorithms. The primary audience for this volume are molecular biologists working either in biotechnology companies or academic research environments, individual researchers and the institutions they work for, and students.
1. Grand challenges in computational biology (D.B. Searls). 2. A tutorial introduction to computation for biologists (S. Salzberg). 3. An introduction to biological sequence analysis (K.H. Fasman, S.L. Salzberg). 4. An introduction to hidden Markov models for biological sequences (A. Krogh). 5. Case-based reasoning driven gene annotation (G.C. Overton, J. Haas). 6. Classification based molecular sequence analysis (D.J. States, W.C. Reisdorf, Jr.) 7. Computational gene prediction using neural networks and similarity search (Y. Xu, E.C. Uberbacher). 8. Modeling dependencies in pre-mRNA splicing signals (C. Burge). 9. Evolutionary approaches to computational biology (R.J. Parsons). 10. Decision trees and Markov chains for gene finding (S. Salzberg). 11. Statistical analysis of protein structures: using environmental features for multiple purposes (L. Wei, J.T. Chang, R.B. Altman). 12. Analysis and algorithms for protein sequence-structure alignment (R.H. Lathrop et al). 13. Threader: protein sequence threading by double dynamic programming (D. Jones). 14. From computer vision to protein structure and association (H.J. Wolfson, R. Nussinov). 15. Modeling biological data and structure with probabilistic networks (S. Kasif, A.L. Delcher). Appendix A. Software and databases for computational biology on the internet. Appendix B: Suggestions for further reading in computational biology.
Handbook / Manual
371 pages, Figs, tabs
...Clear and easy to follow, designed specifically for the non computer scientist, it will help biologists make better choices on which algorithm to use. Documentation (...)an impressive selection of papers, all authored by leading bioinformaticians. The volume covers this large multi-disciplinary field of research quite well, from sequence alignment, pattern discovery, gene finding to protein structure. Trends in Genetics (A. Lloyd, INCBI, the Irish EMBnet Node) The editors have decided to choose authors who have contributed effective software to the community. This horse's mouth approach has the advantage of getting really authoritative explanations of the problems and their solutions.(...)significant effort has been made to introduce each topic in general terms before ploughing deeper into the structure, constraints and functions of biological molecules. Briefings In Bioinformatics, Vol. 1, No. 3