Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,291

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Survey of Branch Support Methods Demonstrates Accuracy, Power, and Robustness of Fast Likelihood-based Approximation Schemes

Open access
Anisimova, Maria, Gil, Manuel, Dufayard, Jean-François, Dessimoz, Christophe, Gascuel, Olivier·2011·Systematic Biology
DOI 10.1093/sysbio/syr041
Open this paper in CoChat — free account

Abstract

Phylogenetic inference and evaluating support for inferred relationships is at the core of many studies testing evolutionary hypotheses. Despite the popularity of nonparametric bootstrap frequencies and Bayesian posterior probabilities, the interpretation of these measures of tree branch support remains a source of discussion. Furthermore, both methods are computationally expensive and become prohibitive for large data sets. Recent fast approximate likelihood-based measures of branch supports (approximate likelihood ratio test [aLRT] and Shimodaira-Hasegawa [SH]-aLRT) provide a compelling alternative to these slower conventional methods, offering not only speed advantages but also excellent levels of accuracy and power. Here we propose an additional method: a Bayesian-like transformation of aLRT (aBayes). Considering both probabilistic and frequentist frameworks, we compare the performance of the three fast likelihood-based methods with the standard bootstrap (SBS), the Bayesian approach, and the recently introduced rapid bootstrap. Our simulations and real data analyses show that with moderate model violations, all tests are sufficiently accurate, but aLRT and aBayes offer the highest statistical power and are very fast. With severe model violations aLRT, aBayes and Bayesian posteriors can produce elevated false-positive rates. With data sets for which such violation can be detected, we recommend using SH-aLRT, the nonparametric version of aLRT based on a procedure similar to the Shimodaira-Hasegawa tree selection. In general, the SBS seems to be excessively conservative and is much slower than our approximate likelihood-based methods.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/sysbio/syr041

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 8 references

Research works printed but not linked: 51

  1. Akaike, 1973, Information theory and an extension of the maximum likelihood principle. Second International Symposium on Information Theory, 267
  2. Aldous, 1996, Probability distributions of cladograms, 1, 10.1007/978-1-4612-0719-1_1 · DOI
  3. Anisimova, 2006, Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative, Syst. Biol., 55, 539, 10.1080/10635150600755453 · DOI · Paper page
  4. Baum, 2005, Evolution. The tree-thinking challenge, Science, 310, 979, 10.1126/science.1117727 · DOI · Paper page
  5. Berry, 1996, On the interpretation of bootstrap trees: appropriate threshold of clade selection and induced gain, Mol. Biol. Evol., 13, 999, 10.1093/molbev/13.7.999 · DOI
  6. Degnan, 2006, Discordance of species trees with their most likely gene trees., PLoS Genet., 2, e68, 10.1371/journal.pgen.0020068 · DOI
  7. Degnan, 2009, Gene tree discordance, phylogenetic inference and the multispecies coalescent, Trends Ecol. Evol., 24, 332, 10.1016/j.tree.2009.01.009 · DOI
  8. Delsuc, 2005, Phylogenomics and the reconstruction of the tree of life, Nat. Rev. Genet., 6, 361, 10.1038/nrg1603 · DOI · Paper page
  9. Desper, 2004, Theoretical foundation of the balanced minimum evolution method of phylogenetic inference and its relationship to weighted least-squares tree fitting, Mol. Biol. Evol., 21, 587, 10.1093/molbev/msh049 · DOI · Paper page
  10. Dessimoz, 2010, Phylogenetic assessment of alignments reveals neglected tree signal in gaps, Genome Biol., 11, R37, 10.1186/gb-2010-11-4-r37 · DOI
  11. Douady, 2003, Comparison of bayesian and maximum likelihood bootstrap measures of phylogenetic reliability, Mol. Biol. Evol., 20, 248, 10.1093/molbev/msg042 · DOI · Paper page
  12. Drummond, 2003, Measurably evolving populations, Trends Ecol. Evol., 18, 481, 10.1016/S0169-5347(03)00216-7 · DOI
  13. Efron, 1979, Bootstrap methods: another look at the jackknife, Ann. Stat, 7, 1, 10.1214/aos/1176344552 · DOI · Paper page
  14. Efron, 1996, Bootstrap confidence levels for phylogenetic trees [corrected and republished article originally printed in Proc. Natl Acad. Sci. U.S.A. 1996, 93:7085–7090], Proc. Natl Acad. Sci. U.S.A., 93, 13429, 10.1073/pnas.93.23.13429 · DOI
  15. Erdos, 1999, A few logs suffice to build (almost) all trees. (i). Random Struct., Algor, 14, 153
  16. Felsenstein, 1985, Confidence limits on phylogenies: an approach using the bootstrap, Evolution, 39, 783, 10.1111/j.1558-5646.1985.tb00420.x · DOI · Paper page
  17. Felsenstein, 2004, Inferring phylogenies
  18. Felsenstein, 1993, Is there something wrong with the bootstrap on phylogenies? A reply to hillis and bull, Syst. Biol., 42, 193, 10.1093/sysbio/42.2.193 · DOI · Paper page
  19. Galtier, 2001, Maximum-likelihood phylogenetic analysis under a covarion-like model, Mol. Biol. Evol., 18, 866, 10.1093/oxfordjournals.molbev.a003868 · DOI · Paper page
  20. Galtier, 2008, Dealing with incongruence in phylogenomic analyses. Philos. Trans. R. Soc. Lond, B Biol. Sci., 363, 4023, 10.1098/rstb.2008.0144 · DOI · Paper page
  21. Guindon, 2003, A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood, Syst. Biol., 52, 696, 10.1080/10635150390235520 · DOI · Paper page
  22. Harding, 1971, The probabilities of rooted tree-shapes generated by random bifurcation, Adv. Appl. Probab, 3, 44, 10.2307/1426329 · DOI · Paper page
  23. Hillis, 1993, An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis, Syst. Biol., 42, 182, 10.1093/sysbio/42.2.182 · DOI
  24. Hordijk, 2005, Improving the efficiency of spr moves in phylogenetic tree search methods based on maximum likelihood, Bioinformatics, 21, 4338, 10.1093/bioinformatics/bti713 · DOI
  25. Huelsenbeck, 2002, Potential applications and pitfalls of Bayesian inference of phylogeny, Syst. Biol., 51, 673, 10.1080/10635150290102366 · DOI · Paper page
  26. Huelsenbeck, 2004, Frequentist properties of bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models, Syst. Biol., 53, 904, 10.1080/10635150490522629 · DOI
  27. Huelsenbeck, 2001, Mrbayes: Bayesian inference of phylogenetic trees, Bioinformatics, 17, 754, 10.1093/bioinformatics/17.8.754 · DOI · Paper page
  28. Joly, 2009, Measuring branch support in species trees obtained by gene tree parsimony, Syst. Biol., 58, 100, 10.1093/sysbio/syp013 · DOI
  29. Kishino, 1989, Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in hominoidea, J. Mol. Evol., 29, 170, 10.1007/BF02100115 · DOI · Paper page
  30. Kolaczkowski, 2007, Effects of branch length uncertainty on Bayesian posterior probabilities for phylogenetic hypotheses, Mol. Biol. Evol., 24, 2108, 10.1093/molbev/msm141 · DOI
  31. Larget, 1999, Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees, Mol. Biol. Evol., 16, 750, 10.1093/oxfordjournals.molbev.a026160 · DOI · Paper page
  32. Lemmon, 2002, The metapopulation genetic algorithm: an efficient solution for the problem of large phylogeny estimation, Proc. Natl Acad. Sci. U.S.A., 99, 10516, 10.1073/pnas.162224399 · DOI
  33. Lewis, 2005, Polytomies and Bayesian phylogenetic inference, Syst. Biol., 54, 241, 10.1080/10635150590924208 · DOI · Paper page
  34. Mau, 1999, Bayesian phylogenetic inference via Markov chain Monte Carlo methods, Biometrics, 55, 1, 10.1111/j.0006-341X.1999.00001.x · DOI
  35. Murphy, 2001, Resolution of the early placental mammal radiation using Bayesian phylogenetics, Science, 294, 2348, 10.1126/science.1067179 · DOI · Paper page
  36. Philippe, 2005, Multigene analyses of bilaterian animals corroborate the monophyly of ecdysozoa, lophotrochozoa, and protostomia, Mol. Biol. Evol., 22, 1246, 10.1093/molbev/msi111 · DOI · Paper page
  37. Phillips, 2001, Mitochondrial genomes of a bandicoot and a brushtail possum confirm the monophyly of australidelphian marsupials, Proc. Biol. Sci., 268, 1533, 10.1098/rspb.2001.1677 · DOI
  38. Pick, 2010, Improved phylogenomic taxon sampling noticeably affects nonbilaterian relationships, Mol Biol Evol, 27, 1983, 10.1093/molbev/msq089 · DOI · Paper page
  39. Posada, 2004, Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests, Syst. Biol., 53, 793, 10.1080/10635150490522304 · DOI · Paper page
  40. Rannala, 1996, Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference, J. Mol. Evol., 43, 304, 10.1007/BF02338839 · DOI · Paper page
  41. Ropiquet, 2009, Supertri: a new approach based on branch support analyses of multiple independent data sets for assessing reliability of phylogenetic inferences., C.R. Biol, 332, 832, 10.1016/j.crvi.2009.05.001 · DOI
  42. Stamatakis, 2008, A rapid bootstrap algorithm for the raxml web servers, Syst. Biol., 57, 758, 10.1080/10635150802429642 · DOI · Paper page
  43. Stamatakis, 2005, Raxml-iii: a fast program for maximum likelihood-based inference of large phylogenetic trees, Bioinformatics, 21, 456, 10.1093/bioinformatics/bti191 · DOI · Paper page
  44. Stamatakis, 2008, Efficient computation of the phylogenetic likelihood function on multi-gene alignments and multi-core architectures. Phil. Trans. R. Soc, Lond. B Biol. Sci., 10.1098/rstb.2008.0163 · DOI
  45. Storey, 2002, A direct approach to false discovery rates. J.R. Stat, Soc B, 64, 479
  46. Strimmer, 2002, Inferring confidence sets of possibly misspecified gene trees, Proc. Biol. Sci., 269, 137, 10.1098/rspb.2001.1862 · DOI
  47. Strimmer, 1997, Likelihood-mapping: a simple method to visualize phylogenetic content of a sequence alignment, Proc. Natl Acad. Sci. U.S.A., 94, 6815, 10.1073/pnas.94.13.6815 · DOI
  48. Wrobel, 2008, Statistical measures of uncertainty for branches in phylogenetic trees inferred from molecular sequences by using model-based methods, J. Appl. Genet., 49, 49, 10.1007/BF03195249 · DOI
  49. Yang, 2007, Fair-balance paradox, star-tree paradox, and Bayesian phylogenetics, Mol. Biol. Evol., 24, 1639, 10.1093/molbev/msm081 · DOI
  50. Yang, 2005, Bayes empirical Bayes inference of amino acid sites under positive selection, Mol. Biol. Evol., 22, 1107, 10.1093/molbev/msi097 · DOI · Paper page
  51. Yule, 1925, A mathematical theory of evolution, based on the conclusions of Dr. J.C. Willis. Phil. Trans. R. Soc. Lond, B Biol. Sci., 213, 21, 10.1098/rstb.1925.0002 · DOI · Paper page

Cited by

Metadata

Authors
Anisimova, M., Gil, M., Dufayard, J.-F., Dessimoz, C., & Gascuel, O.
Venue
Systematic Biology
Publisher
Oxford University Press (OUP)
Published
2011-05-03
Volume
60
Issue
5
Pages
685-699
ISSN
  • 1063-5157 (ISSN-L)
  • 1076-836X
DOI
10.1093/sysbio/syr041
OpenAlex
W2139146574
Cited by
1,291
References
8
FWCI
11.64
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.