Need Advice On Performing Bayesian Model Selection Using The Mrbayes Tool.

I'm just biologist and not an expert in Bayesian statistics, but try to learn how to properly and wisely use the bayesian approach. So, for my concatenated matrix (mtDNA, nuclearDNA + gaps in one of nuclear genes) I made partitioned analysis.

Fully resolved tree topology were yielded with matrix dissected on 9 partitions. To all of them were selected models of nucleotide substitutions (with jModelTest).

gene 1 1st nucleotide in codon
gene 1 2nd nucleotide in codon
gene 1 3st nucleotide in codon
gene 2 1st nucleotide in codon
gene 2 2nd nucleotide in codon
gene 2 3rd nucleotide in codon
gene 3 (rDNA)
gene 4 (rDNA)
gaps (as "standard" characters)

then

lset applyto=(1) nst=1 rates=gamma;
lset applyto=(2) nst=2 rates=equal;
lset applyto=(3) nst=6 rates=gamma;
lset applyto=(4) nst=1 rates=equal;
lset applyto=(5) nst=1 rates=propinv;
lset applyto=(6) nst=1 rates=propinv;
lset applyto=(7) nst=1 rates=gamma;
lset applyto=(8) nst=2 rates=equal;

After a while, I found Jeremy M. Brown's and Fredrik Ronquist's presentations, where they recommended not to choose models, but "let the [bayesian] analysis sample different models... (reversible jump)" and "If you use ModelTest or MrModelTest: Do not fix parameters in MrBayes"

Does it mean for me, that I did everything wrong? With such too detailed partitition I reсieve distorted tree topology, I guess. And can someone explain why is it need to find models by the Bayesian MCMC analysis itself?

Thank you for your attention. I will be glad to hear any comments regarding my analysis.

phylogenetics

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updated 13.5 years ago by Whetting

• written 13.5 years ago by Alice

1 answer

Hi Alice,
First of all, I do not think you did everything (or anything for that matter) wrong in the analysis.
Even with the "lset nst= mixed" command you still need to specify different models of rate variation across sites. So I think it is ok to use ModelTest models in a Bayesian analysis. With the obvious caveat that a model is just that, and can over- or underestimate what is really happening.

As to why you would want MrBayes to test all the models was suggested in their paper (Huelsenbeck et al., 2004): "Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described."

I obviously do not know what tree you are running, but use common sense. The eyeball test (i.e. are things that should be together together ...) can go a long way.
Most importantly, make sure your trees have converged. I like to use AWTY (http://king2.scs.fsu.edu/CEBProjects/awty/awty_start.php) for that.

• 161 views • link

written 13.5 years ago by Whetting

Thank you very much, I can breathe a sigh of relief. I haven't receive tree yet and look forward to SD of split frequencies reach the point of 0.01. Few days ago I met with AWTY, and obtained this picture (after 1 800 000 generations, 0.03 SD, ~150 sequences in analysis, relburnin=yes burninfrac=0.10 printfreq=1000 samplefreq=10000 nchains=4, 2 runs) http://www.flickr.com/photos/papilio_bianor/8496929487/

Because of my big sample I'm going to optimize analysis by playing with chain temp. and number of chains, hope this will help.

Оnce again, thank you for answer.

• 0 views • link

written 13.5 years ago by Alice