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E-book: Phylogenetic Networks: Concepts, Algorithms and Applications

3.60/5 (10 ratings by Goodreads)
(Eberhard-Karls-Universität Tübingen, Germany), (Eberhard-Karls-Universität Tübingen, Germany), (Eberhard-Karls-Universität Tübingen, Germany)
  • Format: PDF+DRM
  • Pub. Date: 02-Dec-2010
  • Publisher: Cambridge University Press
  • Language: eng
  • ISBN-13: 9780511922428
  • Format - PDF+DRM
  • Price: 74,09 €*
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  • Format: PDF+DRM
  • Pub. Date: 02-Dec-2010
  • Publisher: Cambridge University Press
  • Language: eng
  • ISBN-13: 9780511922428

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"In the first part of this book we give an introduction to basic concepts from graph theory and systematics (Chapter 1). We briefly discuss the problem of aligning molecular sequences (Chapter 2) and give a more detailed introduction to the computation of phylogenetic trees from aligned sequences and distances (Chapter 3). Finally, we give a brief introduction to the computation of phyloge-netic networks, which also serves as an overview for the material presented in the second and third parts of the book (Chapter 4). Chapters 2 and 3 are provided for the sake of completeness and reference. They can be skipped by readers who have a basic knowledge of phylogenetic "--

"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 informationand links are available from the book's companion website at www.phylogenetic-networks.org"--

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Reviews

'Networks - rather than just trees - are fast becoming the essential tool for making sense of the complexities of evolution and conflicting signal[ s] 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[ s] and illustrations make the text easy to follow. This book will further transform the way biologists use genomic data to study evolution. The Tübingen 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 H. 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, University of Texas 'This wonderfully accessible book is by far the most thorough and up-to-date treatment of phylogenetic 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 Düsseldorf ' a brave and ambitious attempt to describe the field of phylogenetic networks anno 2012 from a motivated algorithmic perspective a formidable achievement 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. an important milestone in the development of the field.' Systematic Biology

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First interdisciplinary overview of phylogenetic networks, covering biological background, the underlying mathematics, the computational algorithms and the software available.
Preface ix
Part I Introduction
1(84)
1 Basics
3(10)
1.1 Overview
3(1)
1.2 Undirected and directed graphs
3(4)
1.3 Trees
7(1)
1.4 Rooted DAGs
8(1)
1.5 Traversals of trees and DAGs
9(2)
1.6 Taxa, clusters, clades and splits
11(2)
2 Sequence alignment
13(10)
2.1 Overview
13(1)
2.2 Pairwise sequence alignment
13(7)
2.3 Multiple sequence alignment
20(3)
3 Phylogenetic trees
23(45)
3.1 Overview
23(1)
3.2 Phylogenetic trees
24(3)
3.3 The number of phylogenetic trees
27(2)
3.4 Models of DNA evolution
29(3)
3.5 The phylogenetic tree reconstruction problem
32(1)
3.6 Sequence-based methods
33(1)
3.7 Maximum parsimony
33(4)
3.8 Branch-swapping methods
37(3)
3.9 Maximum likelihood estimation
40(3)
3.10 Bootstrap analysis
43(2)
3.11 Bayesian methods
45(5)
3.12 Distance-based methods
50(2)
3.13 UPGMA
52(2)
3.14 Neighbor-joining
54(2)
3.15 Balanced minimum evolution
56(4)
3.16 Comparing trees
60(3)
3.17 Consensus trees
63(3)
3.18 The Newick format
66(2)
4 Introduction to phylogenetic networks
68(17)
4.1 Overview
69(1)
4.2 What is a phylogenetic network?
69(2)
4.3 Unrooted phylogenetic networks
71(5)
4.4 Rooted phylogenetic networks
76(5)
4.5 The extended Newick format
81(2)
4.6 Which types of networks are currently used in practice?
83(2)
Part II Theory
85(100)
5 Splits and unrooted phylogenetic networks
87(40)
5.1 Overview
87(1)
5.2 Splits
88(2)
5.3 Compatibility and incompatibility
90(1)
5.4 Splits and clusters
91(2)
5.5 Split networks
93(4)
5.6 The canonical split network
97(5)
5.7 Circular splits and planar split networks
102(3)
5.8 Weak compatibility
105(2)
5.9 The split decomposition
107(14)
5.10 Representing trees in a split network
121(1)
5.11 Comparing split networks
122(1)
5.12 T-theory
122(5)
6 Clusters and rooted phylogenetic networks
127(58)
6.1 Overview
127(1)
6.2 Clusters, compatibility and incompatibility
128(4)
6.3 Hasse diagrams
132(1)
6.4 Cluster networks
133(5)
6.5 Rooted phylogenetic networks
138(2)
6.6 The lowest stable ancestor
140(4)
6.7 Representing trees in rooted networks
144(2)
6.8 Hardwired and softwired clusters
146(3)
6.9 Minimum rooted phylogenetic networks
149(1)
6.10 Decomposability
150(6)
6.11 Topological constraints on rooted networks
156(12)
6.12 Cluster containment in rooted networks
168(3)
6.13 Tree containment
171(1)
6.14 Comparing rooted networks
171(14)
Part III Algorithms and applications
185(153)
7 Phylogenetic networks from splits
187(6)
7.1 The convex hull algorithm
187(3)
7.2 The circular network algorithm
190(3)
8 Phylogenetic networks from clusters
193(23)
8.1 Cluster networks
193(1)
8.2 Divide-and-conquer using decomposition
194(4)
8.3 Galled trees
198(3)
8.4 Galled networks
201(9)
8.5 Level-k networks
210(6)
9 Phylogenetic networks from sequences
216(34)
9.1 Condensed alignments
216(1)
9.2 Binary sequences and splits
216(2)
9.3 Parsimony splits
218(1)
9.4 Median networks
219(4)
9.5 Quasi-median networks
223(4)
9.6 Median-joining
227(5)
9.7 Pruned quasi-median networks
232(1)
9.8 Recombination networks
233(7)
9.9 Galled trees
240(10)
10 Phylogenetic networks from distances
250(15)
10.1 Distances and splits
250(1)
10.2 Minimum spanning networks
251(1)
10.3 Split decomposition
251(3)
10.4 Neighbor-net
254(7)
10.5 T-Rex
261(4)
11 Phylogenetic networks from trees
265(35)
11.1 Consensus split networks
265(3)
11.2 Consensus super split networks for unrooted trees
268(5)
11.3 Distortion-filtered super split networks for unrooted trees
273(1)
11.4 Consensus cluster networks for rooted trees
274(1)
11.5 Minimum hybridization networks
275(10)
11.6 Minimum hybridization networks and galled trees
285(2)
11.7 Networks from multi-labeled trees
287(2)
11.8 DLT reconciliation of gene and species trees
289(11)
12 Phylogenetic networks from triples or quartets
300(12)
12.1 Trees from rooted triples
300(2)
12.2 Level-k networks from rooted triples
302(6)
12.3 The quartet-net method
308(4)
13 Drawing phylogenetic networks
312(20)
13.1 Overview
312(1)
13.2 Cladograms for rooted phylogenetic trees
312(4)
13.3 Cladograms for rooted phylogenetic networks
316(7)
13.4 Phylograms for rooted phylogenetic trees
323(1)
13.5 Phylograms for rooted phylogenetic networks
324(3)
13.6 Drawing rooted phylogenetic networks with transfer edges
327(1)
13.7 Radial diagrams for unrooted trees
328(1)
13.8 Radial diagrams for split networks
329(3)
14 Software
332(6)
14.1 SplitsTree
332(1)
14.2 Network
333(1)
14.3 TCS
334(1)
14.4 Dendroscope
334(1)
14.5 Other programs
335(3)
Glossary 338(5)
References 343(15)
Index 358
Daniel H. Huson is Professor of Algorithms in Bioinformatics at Tübingen 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 Tübingen 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 Tübingen University. She received her PhD in Computer Science from Montpellier University in 2009.