Simulation-Based Evaluation of Hybridization Network Reconstruction Methods in the Presence of Incomplete Lineage Sorting
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Olga K. Kamneva, Noah A. Rosenberg
Evolutionary Bioinformatics 2017:13 1176934317691935
Published on 10 Mar 2017
Hybridization events generate reticulate species relationships, giving rise to species networks rather than species trees. We report a comparative study of consensus, maximum parsimony, and maximum likelihood methods of species network reconstruction using gene trees simulated assuming a known species history. We evaluate the role of the divergence time between species involved in a hybridization event, the relative contributions of the hybridizing species, and the error in gene tree estimation. When gene tree discordance is mostly due to hybridization and not due to incomplete lineage sorting (ILS), most of the methods can detect even highly skewed hybridization events between highly divergent species. For recent divergences between hybridizing species, when the influence of ILS is sufficiently high, likelihood methods outperform parsimony and consensus methods, which erroneously identify extra hybridizations. The more sophisticated likelihood methods, however, are affected by gene tree errors to a greater extent than are consensus and parsimony.
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DESTAQUE DESTE BLOGGER:
Destaco para os leitores a compreensão de David Morrison, da Universidade Upsalla, Suécia, sobre a pesquisa acima:
My interpretations of the results from the particular scenarios explored by Kamneva and Rosenberg are:
Most of the methods improve as the internal network edges increase in length.
Most of the methods improve as the number of gene trees increases.
Under good conditions the maximum-likelihood methods do better than the parsimony and consensus methods.
The maximum-likelihood methods are more affected by gene-tree error than are the other methods.
There are conditions under which none of the methods work well.
in "Detecting introgression versus hybridization", no blog The Genealogical World of Phylogenetic Network.