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)
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.