Mercurial > repos > iuc > hyphy_fubar

comparison hyphy_fubar.xml @ 2:6943fbec145c draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit 533e8f97b4df382944ac8a31d98e04c9efeb6798"
author iuc
date Thu, 13 Feb 2020 15:01:46 -0500
parents 0f108ee932ea
children b73b35db4664
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1:0f108ee932ea 2:6943fbec145c
50 </tests> 50 </tests>
51 <help><![CDATA[ 51 <help><![CDATA[
52 Model-based selection analyses (such as those performed by PAML and HyPhy) can be slow, becoming impractical for large alignments. We present a method to model and detect selection much faster than existing methods and to leverage Bayesian MCMC to robustly account for parameter estimation errors. 52 Model-based selection analyses (such as those performed by PAML and HyPhy) can be slow, becoming impractical for large alignments. We present a method to model and detect selection much faster than existing methods and to leverage Bayesian MCMC to robustly account for parameter estimation errors.
53 53
54 Results: By exploiting some commonly used approximations, FUBAR can perform detection of positive selection under a model that allows rich site- to-site rate variation about 30 to 50 times faster than existing random effects likelihood methods, and 10 to 30 times faster than existing fixed effects likelihood methods. We introduce an ultra-fast MCMC routine that allows a flexible prior specification, with no parametric constraints on the prior shape. Furthermore, our method allows us to visualize Bayesian inference for each site, revealing the model supported by the data. 54 Results: By exploiting some commonly used approximations, FUBAR can perform detection of positive selection under a model that allows rich site- to-site rate variation about 30 to 50 times faster than existing random effects likelihood methods, and 10 to 30 times faster than existing fixed effects likelihood methods. We introduce an ultra-fast MCMC routine that allows a flexible prior specification, with no parametric constraints on the prior shape. Furthermore, our method allows us to visualize Bayesian inference for each site, revealing the model supported by the data.
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56 See the online documentation_ for more information.
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58 .. _documentation: http://hyphy.org/methods/selection-methods/#fubar
55 ]]></help> 59 ]]></help>
56 <expand macro="citations"> 60 <expand macro="citations">
57 <citation type="doi">10.1093/molbev/mst030</citation> 61 <citation type="doi">10.1093/molbev/mst030</citation>
58 </expand> 62 </expand>
59 </tool> 63 </tool>