Bioinformatics and Molecular Evolution
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Product details
- ISBN 9781405106832
- Weight: 680g
- Dimensions: 188 x 244mm
- Publication Date: 15 Dec 2004
- Publisher: John Wiley and Sons Ltd
- Publication City/Country: GB
- Product Form: Paperback
In the current era of complete genome sequencing, Bioinformatics and Molecular Evolution provides an up-to-date and comprehensive introduction to bioinformatics in the context of evolutionary biology.
This accessible text:
- provides a thorough examination of sequence analysis, biological databases, pattern recognition, and applications to genomics, microarrays, and proteomics
- emphasizes the theoretical and statistical methods used in bioinformatics programs in a way that is accessible to biological science students
- places bioinformatics in the context of evolutionary biology, including population genetics, molecular evolution, molecular phylogenetics, and their applications
- features end-of-chapter problems and self-tests to help students synthesize the materials and apply their understanding
- is accompanied by a dedicated website - www.blackwellpublishing.com/higgs - containing downloadable sequences, links to web resources, answers to self-test questions, and all artwork in downloadable format (artwork also available to instructors on CD-ROM).
This important textbook will equip readers with a thorough understanding of the quantitative methods used in the analysis of molecular evolution, and will be essential reading for advanced undergraduates, graduates, and researchers in molecular biology, genetics, genomics, computational biology, and bioinformatics courses.
Teresa Attwood is Professor of Bioinformatics at Manchester University, Visiting Fellow at the European Bioinformatics Institute, and author of the PRINTS protein fingerprint database. She has a PhD in biophysics from the University of Leeds, and now teaches national and international courses in bioinformatics. Her current research interests include protein sequence analysis, data visualization, and biomedical text mining.
