High Performance Computational Methods for Biological Sequence Analysis / Edition 1

by Tieng K. Yap, Ophir Frieder, Robert L. Martino

Overview

High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field.
This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes.
High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.

Product Details

ISBN-13:	9780792397243
Publisher:	Springer US
Publication date:	04/30/1996
Edition description:	1996
Pages:	212
Product dimensions:	6.14(w) x 9.21(h) x 0.24(d)

Table of Contents

1 Introduction.- 1.1 The Human Genome Project.- 1.2 Informatics.- 1.3 Summary.- 2 Biological Background.- 2.1 Introduction.- 2.2 Types of biological sequences.- 2.3 DNA extraction and amplification.- 2.4 Sequencing technology.- 2.5 Sequence databases.- 2.6 Summary.- 3 Sequence Analysis Algorithms.- 3.1 Introduction.- 3.2 Substitution score matrices.- 3.3 Pairwise sequence alignment.- 3.4 Multiple sequence alignment.- 3.5 Sequence similarity searching.- 3.6 Summary.- 4 High Performance Computing Architectures and Techniques.- 4.1 Introduction.- 4.2 Parallelism: what and why?.- 4.3 Types of computation.- 4.4 Classification schemes.- 4.5 Metrics.- 4.6 Performance related factors.- 4.7 Summary.- 5 Multiprocessor Sequence Alignment.- 5.1 Introduction.- 5.2 Pairwise sequence alignment.- 5.3 Multiple sequence alignment.- 5.4 Summary.- 6 Multiprocessor Sequence Similarity Searching.- 6.1 Introduction.- 6.2 Fine-grain approach.- 6.3 Coarse-grain approach.- 6.4 Summary.- 7 Biological Sequence Resources on the Internet.- 7.1 Introduction.- 7.2 Accessing tools.- 7.3 Search engines.- 7.4 Summary.- 8 Looking to the Future.- References.

Editorial Reviews

Introduces genomic sequencing from an approach that integrates biological, mathematical, and computational concepts. Emphasizes high-performance techniques for computationally intensive DNA sequence analysis that are becoming increasingly important in molecular biology, including pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. The approaches can reduce analysis times significantly, sometimes from days to minutes. For biomedical students and researchers wanting to apply sequence analysis, and students and researchers in computational science wanting to apply their techniques to biomedicine. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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