Dear experts, I am trying to understand the meaning of the following output from a MrBayes run. Input alignment is aminoacid sequence and ProtTest said that Jones+G would be the most suitable aminoacid model for the dataset. In MrBayes I used the aminoacid prior 'mixedmodel' which takes all implemented models into account and decides on the fly which is best for the respective current tree. The acceptance rates for the cold chain of the two runssay this:

Acceptance rates for the moves in the "cold" chain of run 1:

With prob. Chain accepted changes to

31.58 % param. 2 (gamma shape) with multiplier

15.12 % param. 3 (topology and branch lengths) with extending TBR

30.95 % param. 3 (topology and branch lengths) with LOCAL

**0.00 % param. 5 (amino acid model) randomly**

Acceptance rates for the moves in the "cold" chain of run 2:

With prob. Chain accepted changes to

33.14 % param. 2 (gamma shape) with multiplier

15.01 % param. 3 (topology and branch lengths) with extending TBR
31.18 % param. 3 (topology and branch lengths) with LOCAL
**0.03 % param. 5 (amino acid model) randomly**

The way I read this is that the aminoacid model prior was not really suitable because the proposed models hardly ever got accepted.

Next, the Aaino acid model probabilities output looks like this:

Model - Post. Probability - Std.Dev.

Poisson - 0.001 - 0.000000

**Jones - 0.012** - 0.017660

Dayhoff - 0.000 - 0.000000

Mtrev - 0.000 - 0.000000

Mtmam - 0.000 - 0.000000

Wag - 0.026 - 0.009183

Rtrev - 0.000 - 0.000000

**Cprev - 0.956** - 0.026843

Vt - 0.000 - 0.000000

Blosum - 0.004 - 0.000000

The way I read this is that apparently the mixed model was pretty much useless because (i) in 95% of the cases Cprev was the best model, and (ii) strangely, Jones hardly ever got used although this was the model suggested by ProtTest. My interpretation is that I should try the next MrBayes run with Cprev as the selected aminoacid model.

Am I somewhat on the right path on how I interpreted this output or am I totally on the wrong track?

Any suggestions are greatly appreciated. Thanks a lot.

Marcel

hey david, thanks a lot for the feedback and the reassurance. what you suggest makes sense to me ...