Linked Life Data

16895925

Source:http://linkedlifedata.com/resource/pubmed/id/16895925

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
20
pubmed:dateCreated
2006-10-11
pubmed:abstractText
Accurate detection of positive Darwinian selection can provide important insights to researchers investigating the evolution of pathogens. However, many pathogens (particularly viruses) undergo frequent recombination and the phylogenetic methods commonly applied to detect positive selection have been shown to give misleading results when applied to recombining sequences. We propose a method that makes maximum likelihood inference of positive selection robust to the presence of recombination. This is achieved by allowing tree topologies and branch lengths to change across detected recombination breakpoints. Further improvements are obtained by allowing synonymous substitution rates to vary across sites.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2493-9
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Robust inference of positive selection from recombining coding sequences.
pubmed:affiliation
Computational Biology Group, Institute of Infectious Disease and Molecular Medicine University of Cape Town, Private Bag, Rondebosch 7701, South Africa. konrad@cbio.uct.ac.za
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural