Literature DB >> 28561973

FACTORS DETERMINING THE ACCURACY OF CLADOGRAM ESTIMATION: EVALUATION USING COMPUTER SIMULATION.

Kent L Fiala1, Robert R Sokal1.   

Abstract

We developed a simulation model of phylogenesis with which we generated a large number of phylogenies and associated data matrices. We examined the characteristics of these and evaluated the success of three taxonomic methods (Wagner parsimony, character compatibility, and UPGMA clustering) as estimators of phylogeny, paying particular attention to the consequences of changes in certain evolutionary assumptions: relative rate of evolution in three different evolutionary contexts (phyletic, parent lineage, and daughter lineage); relative rate of evolution in different directions (novel forward, convergent forward, or reverse); variation of evolutionary rates; and topology of the phylogenetic tree. Except for variation of evolutionary rates, all the evolutionary parameters that we controlled had significant effects on accuracy of phylogenetic reconstructions. Unexpectedly, the topology of the phylogeny was the most important single factor affecting accuracy; some phylogenies are more readily estimated than others for simply historical reasons. We conclude that none of the three estimation methods is very accurate, that the differences in accuracy among them are rather small, and that historical effects (the branching pattern of a phylogeny) may outweigh biological effects in determining the accuracy with which a phylogeny can be reconstructed. © 1985 The Society for the Study of Evolution.

Year:  1985        PMID: 28561973     DOI: 10.1111/j.1558-5646.1985.tb00398.x

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  11 in total

1.  A Simulation-Based Evaluation of Tip-Dating Under the Fossilized Birth-Death Process.

Authors:  Arong Luo; David A Duchêne; Chi Zhang; Chao-Dong Zhu; Simon Y W Ho
Journal:  Syst Biol       Date:  2020-03-01       Impact factor: 15.683

2.  Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations.

Authors:  Joseph N Keating; Robert S Sansom; Mark D Sutton; Christopher G Knight; Russell J Garwood
Journal:  Syst Biol       Date:  2020-09-01       Impact factor: 15.683

3.  Monte Carlo simulation in phylogenies: an application to test the constancy of evolutionary rates.

Authors:  J C Adell; J Dopazo
Journal:  J Mol Evol       Date:  1994-03       Impact factor: 2.395

4.  Excluding Loci With Substitution Saturation Improves Inferences From Phylogenomic Data.

Authors:  David A Duchêne; Niklas Mather; Cara Van Der Wal; Simon Y W Ho
Journal:  Syst Biol       Date:  2022-04-19       Impact factor: 9.160

5.  Evolutionary Rate Variation among Lineages in Gene Trees has a Negative Impact on Species-Tree Inference.

Authors:  Mezzalina Vankan; Simon Y W Ho; David A Duchêne
Journal:  Syst Biol       Date:  2022-02-10       Impact factor: 15.683

6.  BIO::Phylo-phyloinformatic analysis using perl.

Authors:  Rutger A Vos; Jason Caravas; Klaas Hartmann; Mark A Jensen; Chase Miller
Journal:  BMC Bioinformatics       Date:  2011-02-27       Impact factor: 3.307

7.  Differences in Performance among Test Statistics for Assessing Phylogenomic Model Adequacy.

Authors:  David A Duchêne; Sebastian Duchêne; Simon Y W Ho
Journal:  Genome Biol Evol       Date:  2018-06-01       Impact factor: 3.416

8.  Genealogical typing of Neisseria meningitidis.

Authors:  Xavier Didelot; Rachel Urwin; Martin C J Maiden; Daniel Falush
Journal:  Microbiology (Reading)       Date:  2009-07-30       Impact factor: 2.777

9.  Automated masking of AFLP markers improves reliability of phylogenetic analyses.

Authors:  Patrick Kück; Carola Greve; Bernhard Misof; France Gimnich
Journal:  PLoS One       Date:  2012-11-09       Impact factor: 3.240

10.  A comparison of methods for estimating substitution rates from ancient DNA sequence data.

Authors:  K Jun Tong; David A Duchêne; Sebastián Duchêne; Jemma L Geoghegan; Simon Y W Ho
Journal:  BMC Evol Biol       Date:  2018-05-16       Impact factor: 3.260

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.