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Phylogenetic Networks: Concepts, Algorithms and Applications, by Daniel H. Huson, Regula Rupp, Dr Celine Scornavacca
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The evolutionary history of species is traditionally represented using a rooted phylogenetic tree. However, when reticulate events such as hybridization, horizontal gene transfer or recombination are believed to be involved, phylogenetic networks that can accommodate non-treelike evolution have an important role to play. This book provides the first interdisciplinary overview of phylogenetic networks. Beginning with a concise introduction to both phylogenetic trees and phylogenetic networks, the fundamental concepts and results are then presented for both rooted and unrooted phylogenetic networks. Current approaches and algorithms available for computing phylogenetic networks from different types of datasets are then discussed, accompanied by examples of their application to real biological datasets. The book also summarises the algorithms used for drawing phylogenetic networks, along with the existing software for their computation and evaluation. All datasets, examples and other additional information and links are available from the book's companion website at www.phylogenetic-networks.org.
- Sales Rank: #1327481 in Books
- Published on: 2011-01-17
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .87" w x 6.85" l, 1.94 pounds
- Binding: Hardcover
- 376 pages
Review
"Networks - rather than just trees - are fast becoming the essential tool for making sense of the complexities of evolution, and conflicting signal in genomic data. Phylogenetic Networks provides a long-overdue exposition of network-based methods, their possible uses, and details on practical software. A detailed and unified treatment of the many different types of networks is complemented by a crisp synopsis of the underlying theory. Numerous example and illustrations make the text easy to follow. This book will further transform the way biologists use genomic data to study evolution. The T�bingen group has led the development of phylogenetic network algorithms, and this book delivers a clear exposition for biologists bewildered by a plethora of recent methods, as well as for bioinformaticians aiming to develop the field further. It is essential reading for any scientist or student seeking to understand how genomic data can be used to represent and study the intricate 'web of life'."
Mike Steel, University of Canterbury
"This textbook, by one of the leaders of the field (Daniel Huson) and his co-authors, provides a mathematically rigorous introduction to one of the most exciting and beautiful research areas in computational biology: phylogenetic networks. The text is clear and provides all the necessary biology background; it should be accessible to graduate students (or upper-division undergraduates) in mathematics, computer science, or statistics."
Tandy Warnow, The University of Texas
"This wonderfully accessible book is by far the most thorough and up to date treatment of phylgenetic networks about. Many evolutionary processes in nature do not conform to the simple model of phylogenetic trees; examples are hybridizations, symbioses, and lateral gene transfer. The more we probe nature with genomics, the more significant and numerous these examples become, so there is a real need for using networks in phylogenetics. This volume is a must for researchers working with phylogenetic networks. It is for an advanced college audience. Beautifully organized and clearly written, it really fills a void."
Bill Martin, University of Duesseldorf
"...a technical monograph that will be of great interest to those working in this field..."
Paul Cull, Computing Reviews
"What has been lacking until now is a full in-depth treatment of phylogenetic networks as they are currently used in the evolutionary literature. The new book by Huson and coworkers nicely fills this niche. The book is a tour de force of explication, making clear, in some cases for the first time, how phylogenetic networks of various sorts are actually formulated, calculated and interpreted. We have needed a comprehensive review of phylogenetic networks like this for some time now, and the authors are to be congratulated for creating a review that is clearly written and attractively presented."
James Whitfield, University of Illinois at Urbana for Systematic Biology
"This book is a brave and ambitious attempt to describe the field of phylogenetic networks anno 2010 from a motivated algorithmic perspective. It will deservedly become essential reading for both mathematically inclined researchers already working in the field and those looking for an easy way to enter the field. Part reference text, part introductory text, the book brings into sharp relief what we do - and do not - know about phylogenetic networks and as such will become an important milestone in the development of the field."
Steven Kalk, Maastricht University for Systematic Biology
"This is an excellent book for readers who know the topic well and also for those who have some knowledge of the area."
Idalia Flores, Computing Reviews
About the Author
Daniel H. Huson is Professor of Algorithms in Bioinformatics at T�bingen University. He has authored numerous papers in bioinformatics, biology and mathematics, and is the main author of the widely-used computer programs Dendroscope, MEGAN and SplitsTree.
Regula Rupp received her PhD in Mathematics from Bern University in 2006. Between 2007 and 2009 she held a postdoctoral research position at T�bingen University, working with Daniel H. Huson in developing robust methods for computing phylogenetic networks from real biological data.
Celine Scornavacca is a postdoctoral researcher working on algorithms for phylogenetic networks with Daniel H. Huson at T�bingen University. She received her PhD in Computer Science from Montpellier University in 2009.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Very technical
By David Baum
The mathematical treatment is extremely thorough. As a biologistI found it rather lacking in relevance because it seemed more concerned with algorithms than with statistical inference of biological facts. But I commend the authors for rigor.
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