Computational Methods in Molecular Biology

by S.L. Salzberg (Editor), D.B. Searls (Editor), S. Kasif (Editor)

Overview

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.
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 will 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. Any biologist who relies on computers should want this book.
A secondary audience will be computer scientists developing techniques with applications in biology.
An excellent reference for leading techniques, it will also help introduce computer scientists to the biology problems. This is an outstanding work which will be ideal for the increasing number of scientists moving into computational biology.

Product Details

ISBN-13:	9780080860930
Publisher:	Elsevier Science
Publication date:	06/19/1998
Series:	New Comprehensive Biochemistry , #32
Sold by:	Barnes & Noble
Format:	eBook
Pages:	368
File size:	7 MB

Table of Contents

Preface. 1. Grand challenges in computational biology (D.B. Searls). 2. A tutorial introduction to computation for biologists (S.L. 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.B. Burge). 9. Evolutionary approaches to computational biology (R.J. Parsons). 10. Decision trees and Markov chains for gene finding (S.L. 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.

Editorial Reviews

4 Stars! from Doody

This book is designed as an introduction to one of the fastest growing fields in modern biology. The information deluge derived from the various sequencing projects (bacteria, yeast, arabidopsis, protozoans, and man) has driven the development of sophisticated tools to analyze and use the data. Many investigators apply available software packages to their own studies but have little understanding of the structure of these packages and the reasoning behind their design. Conversely, mathematical and computer literate scientists often have limited understanding of the interests of biologists in these huge databases. Hence the current volume provides readers both the technical understanding and practical value of the present approaches to information management at this scale. The intended audience is advanced students, fellows, and investigators in molecular biology. Two early chapters serve as tutorials—one for biologists and one for mathematicians/computer scientists. Strategies for both protein and nucleic acid analysis are discussed in subsequent chapters with attention given to pattern recognition, homologies, and evolution—all key issues. While some of the material is strongly mathematical in content, this does not detract from the value of this book; it can serve to provide an appreciation of both the power and limitations of the various approaches. Few modern biology laboratories can afford to ignore this aspect of the science and all investigators can profit from the material covered. The text would also be very suitable for an advanced course in bioinformatics.

Eugene A. Davidson

This volume first provides an overview of the main challenges facing computational biology and presents a tutorial on computation for molecular biologists (the volume's primary intended audience) and a tutorial for computer scientists on the biology underlying the research problems in later chapters. Next, contributions addressing DNA and RNA sequence analysis are presented, including a tutorial introduction to hidden Markov models, a case-based reasoning approach to sequence annotation, description of how to use sequence similarity as the basis for sequence classification, details about the latest version of GRAIL, discussion of how to model RNA splicing signals using statistical patterns, and tutorial material on genetic algorithms and decision trees. Remaining contributions address proteins, including treatment of statistical methods for protein structure analysis, the THREADER system, a novel application of geometric hashing, and a probabilistic modeling approach similar to HMMs. An appendix briefly describes software and databases for computational biology on the Internet and offers suggestions for further reading. Annotation c. by Book News, Inc., Portland, Or.

Booknews

Reviewer: Eugene A Davidson, PhD (Georgetown University School of Medicine)
Description: This book is designed as an introduction to one of the fastest growing fields in modern biology. The information deluge derived from the various sequencing projects (bacteria, yeast, arabidopsis, protozoans, and man) has driven the development of sophisticated tools to analyze and use the data.
Purpose: Many investigators apply available software packages to their own studies but have little understanding of the structure of these packages and the reasoning behind their design. Conversely, mathematical and computer literate scientists often have limited understanding of the interests of biologists in these huge databases. Hence the current volume provides readers both the technical understanding and practical value of the present approaches to information management at this scale.
Audience: The intended audience is advanced students, fellows, and investigators in molecular biology.
Features: Two early chapters serve as tutorials — one for biologists and one for mathematicians/computer scientists. Strategies for both protein and nucleic acid analysis are discussed in subsequent chapters with attention given to pattern recognition, homologies, and evolution — all key issues. While some of the material is strongly mathematical in content, this does not detract from the value of this book; it can serve to provide an appreciation of both the power and limitations of the various approaches.
Assessment: Few modern biology laboratories can afford to ignore this aspect of the science and all investigators can profit from the material covered. The text would also be very suitable for an advanced course in bioinformatics.

Doody's Review Service

...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

From the Publisher