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Bioinformatics

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Attention for Chapter 17: Detecting and Analyzing Genetic Recombination Using RDP4.
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Chapter title
Detecting and Analyzing Genetic Recombination Using RDP4.
Chapter number 17
Book title
Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6622-6_17
Pubmed ID
Book ISBNs
978-1-4939-6620-2, 978-1-4939-6622-6
Authors

Darren P. Martin, Ben Murrell, Arjun Khoosal, Brejnev Muhire

Editors

Jonathan M. Keith

Abstract

Recombination between nucleotide sequences is a major process influencing the evolution of most species on Earth. The evolutionary value of recombination has been widely debated and so too has its influence on evolutionary analysis methods that assume nucleotide sequences replicate without recombining. When nucleic acids recombine, the evolution of the daughter or recombinant molecule cannot be accurately described by a single phylogeny. This simple fact can seriously undermine the accuracy of any phylogenetics-based analytical approach which assumes that the evolutionary history of a set of recombining sequences can be adequately described by a single phylogenetic tree. There are presently a large number of available methods and associated computer programs for analyzing and characterizing recombination in various classes of nucleotide sequence datasets. Here we examine the use of some of these methods to derive and test recombination hypotheses using multiple sequence alignments.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 25%
Student > Ph. D. Student 10 20%
Researcher 9 18%
Student > Doctoral Student 3 6%
Lecturer > Senior Lecturer 2 4%
Other 4 8%
Unknown 10 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 24%
Agricultural and Biological Sciences 12 24%
Veterinary Science and Veterinary Medicine 3 6%
Medicine and Dentistry 3 6%
Computer Science 2 4%
Other 4 8%
Unknown 15 29%