Evidence map›Paper›PMID 32410614›Full record

ArticleBMC evolutionary biology2020

Improving the performance of Bayesian phylogenetic inference under relaxed clock models.

Rong Zhang, Alexei Drummond

Abstract read
In one paragraph

Article in BMC evolutionary biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
  2. Review
  3. Navigating the mysterious space of evolutionary histories.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Biological causes and impacts of rugged tree landscapes in phylodynamic inference.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Dated Phylogeny ofBiology · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Rong ZhangSchool of Computer Science, University of Auckland, Auckland, New Zealand. rzha419@aucklanduni.ac.nz.
Alexei DrummondSchool of Computer Science, University of Auckland, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBayesian MCMC has become a common approach for phylogenetic inference. But the growing size of molecular sequence data sets has created a pressing need to improve the computational efficiency of Bayesian phylogenetic inference algorithms.

resultsThis paper develops a new algorithm to improve the efficiency of Bayesian phylogenetic inference for models that include a per-branch rate parameter. In a Markov chain Monte Carlo algorithm, the presented proposal kernel changes evolutionary rates and divergence times at the same time, under the constraint that the implied genetic distances remain constant. Specifically, the proposal operates on the divergence time of an internal node and the three adjacent branch rates. For the root of a phylogenetic tree, there are three strategies discussed, named Simple Distance, Small Pulley and Big Pulley. Note that Big Pulley is able to change the tree topology, which enables the operator to sample all the possible rooted trees consistent with the implied unrooted tree. To validate its effectiveness, a series of experiments have been performed by implementing the proposed operator in the BEAST2 software.

conclusionsThe results demonstrate that the proposed operator is able to improve the performance by giving better estimates for a given chain length and by using less running time for a given level of accuracy. Measured by effective samples per hour, use of the proposed operator results in overall mixing more efficient than the current operators in BEAST2. Especially for large data sets, the improvement is up to half an order of magnitude.

Indexed as

Models, GeneticPhylogenyAlgorithmsBayes TheoremCalibrationComputer SimulationMarkov ChainsMonte Carlo MethodTime FactorsBayesian MCMCBayesian phylogeneticsDivergence timesEvolutionary ratesGenetic distancesProposal kernel

Identifiers

PMID32410614
PMCPMC7222466

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.