Why weight?

Whether phylogenetic data should be differentially or equally weighted is currently debated. Further, if differential weighting is to be explored, there is no consensus among investigators as to which weighting scheme is most appropriate. Mitochondrial genome data offer a powerful tool in assessment...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Molecular phylogenetics and evolution 2007-06, Vol.43 (3), p.999-1004
Hauptverfasser: Kjer, Karl M., Swigonova, Zuzana, LaPolla, John S., Broughton, Richard E.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Whether phylogenetic data should be differentially or equally weighted is currently debated. Further, if differential weighting is to be explored, there is no consensus among investigators as to which weighting scheme is most appropriate. Mitochondrial genome data offer a powerful tool in assessment of differential weighting schemes because taxa can be selected from which a highly corroborated phylogeny is available (so that accuracy can be assessed), and it can be assumed that different data partitions share the same history (so that gene-sorting issues are not so problematic). Using mitochondrial data from 17 mammalian genomes, we evaluated the most commonly used weighting schemes, such as successive weighting, transversion weighting, codon-based weighting, and amino acid coding, and compared them to more complex weighting schemes including a 6-parameter weighting, pseudoreplicate reweighting, and tri-level weighting. We found that the most commonly used weighting schemes perform the worst with these data. Some of the more complex schemes perform well, however, none of them is consistently superior. These results support ones biases; if one has a predilection to avoid differential weighting, these data support equally weighted parsimony and maximum likelihood. Others might be encouraged by these results to try weighting as a form of data exploration.
ISSN:1055-7903
1095-9513
DOI:10.1016/j.ympev.2007.02.028