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model designation (Read 3014 times)
cheilarocha
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model designation
Jun 27th, 2006 at 11:20am
 
I would like to know the 6 character designation of the model GTR-G. Thank you!
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Sergei
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Datamonkeys are forever...

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Re: model designation
Reply #1 - Jun 27th, 2006 at 12:25pm
 
Dear cheilarocha,

Quote:
I would like to know the 6 character designation of the model GTR-G. Thank you!


You can use the string 012345 to designate GTR (6 different rates). The '-G' part is specified in another dialog (site-to-site rate variation), where you would choose Gamma (we actually recommend the Beta-Gamma extension, as it nearly always outperforms the Gamma).

Which analyses are you trying to specify the model for?

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Sergei
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cheila
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Re: model designation
Reply #2 - Jun 27th, 2006 at 3:54pm
 
you are fast! thank you!

I am performing the analyses of positive selection with NielsenYang method but the best model for my samples (model test performed with PAML) is GTR-G. what should I do? use GTR (012345) or another?

all the best!

Cheila
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Sergei
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Re: model designation
Reply #3 - Jun 27th, 2006 at 4:34pm
 
Dear Cheila,

In this case the -G is irrelevant. NielsenYang.bf uses codon models of evolution which include their own way to correct for rate variation across sites (this is actually the basis for detecting codon-level positive selection). The -G part only applies if you want to use nucleotide models to analyze your data. 012345 would be the appropriate input (using, for example, the MG94 custom model).

If you'd like, you can also perform model selection and analysis for positive selection using Multimedia File Viewing and Clickable Links are available for Registered Members only!!  You need to Login Login, which implements three different methods for finding positively and negatively selected codons. The original Nielsen-Yang can falsely identify hypervariable sites (e.g. relaxed functional constraint, along with elevated mutation rates) as positively selected. We recommend the use of models which allow both synonymous and non-synonymous rates to vary from codon to codon (NY assumes that synonymous rates are constant = 1, and non-synonymous rates vary from codon to codon).

If you are interested, or just want to learn more about how modeling assumptions can affect selection analyses, I would suggest that you take a look at Multimedia File Viewing and Clickable Links are available for Registered Members only!!  You need to Login Login and Multimedia File Viewing and Clickable Links are available for Registered Members only!!  You need to Login Login

Cheers,
Sergei
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Associate Professor
Division of Infectious Diseases
Division of Biomedical Informatics
School of Medicine
University of California San Diego
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