Date: Thu, 28 Mar 2024 19:02:27 +0700 (NOVT)
Message-ID: <212008217.86494.1711627347809@ugene.net>
Subject: Exported From Confluence
MIME-Version: 1.0
Content-Type: multipart/related;
boundary="----=_Part_86493_905257944.1711627347809"
------=_Part_86493_905257944.1711627347809
Content-Type: text/html; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
Content-Location: file:///C:/exported.html
The Building Phylogenetic Tree dialog for the =
MrBayes method has the following view:
=20
=20
=
=20
=20
There are two steps to a phylogenetic ana=
lysis using MrBayes:
- Set the evolutionary model.
- Run the Markov chain Monte Carlo (MCMC) analisys.
The evolutionary model is defined by the =
following parameters:
Sub=
stitution model =E2=80=94 specifies the general structure of a DN=
A substitution model. This parameter is available for the nucleotide sequen=
ces. It corresponds to the Nst setting of MrBayes. You may select one of th=
e following:
-
- JC69 (Nst=3D1)
- HKY85 (Nst=3D2)
- GTR (Nst=3D6)
Rat=
e matrix (fixed) =E2=80=94 specifies the fixed-rate amino-acid mo=
del. This parameter is available for amino-acid sequences. The following mo=
dels are available:
-
- poisson
- jones
- dayhoff
- mtrev
- mtmam
- wag
- rtrev
- cprev
- vt
- blosum
- equaline
The following parameters are common for n=
ucleotide and amino-acid sequences:
Rat=
e =E2=80=94 sets the model for among-site rate variation. Select =
one of the following:
-
- equal =E2=80=94 no rate variation across sites.
- gamma =E2=80=94 gamma-distributed rates across sites. The rate at a sit=
e is drawn from a gamma distribution. The gamma distribution has a single p=
arameter that describes how much rates vary.
- propinv =E2=80=94 a proportion of the sites are invariable.
- invgamma =E2=80=94 a proportion of the sites are invariable while the r=
ate for the remaining sites are drawn from a gamma distribution.
Gam=
ma =E2=80=94 sets the number of rate categories for the gamma dis=
tribution.
You can select the fo=
llowing parameters for the MCMC analisys:
Cha=
in length =E2=80=94 sets the number of cycles for the MCMC algori=
thm. This should be a big number as you want the chain to first reach stati=
onarity, and then remain there for enough time to take lots of samples.
Sub=
sampling frequency =E2=80=94 specifies how often the Markov chain=
is sampled. You can sample the chain every cycle, but this results in very=
large output files.
Bur=
n-in length =E2=80=94 determines the number of samples that will =
be discarded when convergence diagnostics are calculated.
Hea=
ted chains =E2=80=94 number of chains will be used in Metropolis =
coupling. Set 1 to use usual MCMC analysis.
Hea=
ted chain temp =E2=80=94 the temperature parameter for heating th=
e chains. The higher the temperature, the more likely the heated chains are=
to move between isolated peaks in the posterior distribution.
Ran=
dom seed =E2=80=94 a seed for the random number generator.
Display tree in n=
ew window - displays tree in new window.
Display tree with=
alignment editor - displays tree with alignment editor.
Synchronize align=
ment with tree - synchronize alignment and tree.
Sav=
e tree to =E2=80=94 file to save the built tree.
Press the Bui=
ld button to run the analysis with the parameters selected and bu=
ild a consensus tree.
------=_Part_86493_905257944.1711627347809
Content-Type: image/png
Content-Transfer-Encoding: base64
Content-Location: file:///C:/cbbb252e36d7cad7c018817c204f12df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------=_Part_86493_905257944.1711627347809--