Bootstrap-based Support of HGT Inferred by Maximum Parsimony
<p>Abstract</p> <p>Background</p> <p>Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of <it>phylogenetic networks</it>, and d...
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doaj-e76e3903aae84ef4becbb380ab6293a42021-09-02T04:30:11ZengBMCBMC Evolutionary Biology1471-21482010-05-0110113110.1186/1471-2148-10-131Bootstrap-based Support of HGT Inferred by Maximum ParsimonyNakhleh LuayJin GuohuaPark Hyun<p>Abstract</p> <p>Background</p> <p>Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of <it>phylogenetic networks</it>, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An <it>ad hoc </it>solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold.</p> <p>Results</p> <p>In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples.</p> <p>Conclusions</p> <p>We have implemented our method in the NEPAL software tool (available publicly at <url>http://bioinfo.cs.rice.edu/</url>), and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.</p> http://www.biomedcentral.com/1471-2148/10/131 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nakhleh Luay Jin Guohua Park Hyun |
spellingShingle |
Nakhleh Luay Jin Guohua Park Hyun Bootstrap-based Support of HGT Inferred by Maximum Parsimony BMC Evolutionary Biology |
author_facet |
Nakhleh Luay Jin Guohua Park Hyun |
author_sort |
Nakhleh Luay |
title |
Bootstrap-based Support of HGT Inferred by Maximum Parsimony |
title_short |
Bootstrap-based Support of HGT Inferred by Maximum Parsimony |
title_full |
Bootstrap-based Support of HGT Inferred by Maximum Parsimony |
title_fullStr |
Bootstrap-based Support of HGT Inferred by Maximum Parsimony |
title_full_unstemmed |
Bootstrap-based Support of HGT Inferred by Maximum Parsimony |
title_sort |
bootstrap-based support of hgt inferred by maximum parsimony |
publisher |
BMC |
series |
BMC Evolutionary Biology |
issn |
1471-2148 |
publishDate |
2010-05-01 |
description |
<p>Abstract</p> <p>Background</p> <p>Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of <it>phylogenetic networks</it>, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An <it>ad hoc </it>solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold.</p> <p>Results</p> <p>In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples.</p> <p>Conclusions</p> <p>We have implemented our method in the NEPAL software tool (available publicly at <url>http://bioinfo.cs.rice.edu/</url>), and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.</p> |
url |
http://www.biomedcentral.com/1471-2148/10/131 |
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