Name
Abstract | ||
|---|---|---|
The capability to conduct Maximum Likelihood based phylogenetic (evolutionary) analyses on datasets that contain both morphological, as well as molecular data partitions with programs such as RAxML, gives rise to new methodological questions. As we demonstrate on 5 real world datasets that comprise morphological as well as DNA data the trees inferred by separately using the morphological or molecular data partitions are highly incongruent. Since in typical current-day phylogenomic alignments, there is significantly more molecular than morphological data available, and hence the final tree shape in a concatenated analysis is dominated by molecular data, the question arises how morphological data can be used within this context. One important application lies in the phylogenetic placement of fossil taxa (for which only morphological data is available) into a fixed, given molecular or otherwise well-established reference tree. By using real and simulated datasets we conduct the first assessment of placement accuracy for fossil taxa under the Maximum Likelihood criterion. We demonstrate that, despite conflicting phylogenetic signals from the morphological and molecular partitions, the Maximum Likelihood criterion is powerful enough to yield accurate fossil placements. Moreover, we develop and make available a new morphological site weight calibration algorithm that yields an average improvement of fossil placement accuracy of 20% on more than 2,500 simulated datasets and of 25% on the 5 real-world datasets that all contain highly conflicting phylogenetic signal. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/AICCSA.2010.5586939 | Computer Systems and Applications |
Keywords | Field | DocType |
new morphological site weight,simulated datasets,fossil taxon,maximum likelihood criterion,dna data,molecular data partition,molecular data,molecular partition,morphological data,morphology-based phylogenetic fossil placement,conflicting phylogenetic signal,maximum likelihood estimation,maximum likelihood,genetics,dna,palaeontology | Calibration algorithm,Phylogenetic inference,Phylogenetic tree,Pattern recognition,Computer science,Maximum likelihood,Real-time computing,Artificial intelligence,Concatenation,Bioinformatics,Taxon,Maximum likelihood criterion | Conference |
ISBN | Citations | PageRank |
978-1-4244-7716-6 | 2 | 1.01 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon A. Berger | 1 | 71 | 7.46 |
| Alexandros Stamatakis | 2 | 995 | 96.27 |