Ancestral Sequence Reconstruction

Ancestral Sequence Reconstruction
Author :
Publisher : Oxford University Press
Total Pages : 267
Release :
ISBN-10 : 9780199299188
ISBN-13 : 0199299188
Rating : 4/5 (88 Downloads)

Book Synopsis Ancestral Sequence Reconstruction by : David A Liberles

Download or read book Ancestral Sequence Reconstruction written by David A Liberles and published by Oxford University Press. This book was released on 2007-05-31 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ancestral sequence reconstruction is a technique of growing importance in molecular evolutionary biology and comparative genomics. As a powerful tool for testing evolutionary and ecological hypotheses, as well as uncovering the link between sequence and molecular phenotype, there are potential applications in a range of fields.Ancestral Sequence Reconstruction starts with a historical overview of the field, before discussing the potential applications in drug discovery and the pharmaceutical industry. This is followed by a section on computational methodology, which provides a detailed discussion of the available methods for reconstructing ancestral sequences (including their advantages, disadvantages, and potential pitfalls). Purely computational applications of the technique are then covered, including wholeproteome reconstruction. Further chapters provide a detailed discussion on taking computationally reconstructed sequences and synthesizing them in the laboratory. The book concludes with a description of the scientific questions where experimental ancestral sequence reconstruction has been utilized toprovide insights and inform future research.This research level text provides a first synthesis of the theories, methodologies and applications associated with ancestral sequence recognition, while simultaneously addressing many of the hot topics in the field. It will be of interest and use to both graduate students and researchers in the fields of molecular biology, molecular evolution, and evolutionary bioinformatics.


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