<p>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.</p>

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

<ol>
<li>gene 1 1st nucleotide in codon</li>
<li>gene 1 2nd nucleotide in codon</li>
<li>gene 1 3st nucleotide in codon</li>
<li>gene 2 1st nucleotide in codon </li>
<li>gene 2 2nd nucleotide in codon</li>
<li>gene 2 3rd nucleotide in codon</li>
<li>gene 3 (rDNA)</li>
<li>gene 4 (rDNA)</li>
<li>gaps (as "standard" characters)</li>
</ol>

<p>then</p>

<pre><code>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;
</code></pre>

<p>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"</p>

<p>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?</p>

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

<p>Hi Alice, <br/>
First of all, I do not think you did everything (or anything for that matter) wrong in the analysis. <br/>
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.   </p>

<p>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."</p>

<p>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. <br/>
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.</p>
