Mercurial > repos > iuc > hyphy_cfel
view test-data/summary/nsp8.combined.fas.BGM.json @ 9:5b63871000c9 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit 4bb2092ac089e709bdc0ec3c3680f47e17be509a"
| author | iuc |
|---|---|
| date | Wed, 30 Mar 2022 00:24:23 +0000 |
| parents | c046f72e5e3a |
| children |
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{ "analysis":{ "authors":"Art FY Poon, Fraser I Lewis, Simon DW Frost and Sergei L Kosakovsky Pond", "citation":"Spidermonkey: rapid detection of co-evolving sites using Bayesian graphical models (2008). _Bioinformatics_ 24(17): 1949-1950", "contact":"apoon42@uwo.ca", "info":"BGM (Bayesian Graphical Model) uses a maximum likelihood ancestral state reconstruction to\nmap substitution (non-synonymous only for coding data) events to branches in the\nphylogeny and then analyzes the joint distribution of the\nsubstitution map using a Bayesian graphical model (network).\nNext, a Markov chain Monte Carlo analysis is used to generate\na random sample of network structures from the posterior\ndistribution given the data. Each node in the network\nrepresents a codon site in the alignment, and links (edges)\nbetween nodes indicate high posterior support for correlated\nsubstitutions at the two sites over time, which implies coevolution.", "requirements":"in-frame codon alignment and a phylogenetic tree", "version":"1.2" }, "branch attributes":{ "0":{ "Node10":{ "models.codon.MG_REV.ModelDescription":0 }, "Node12":{ "models.codon.MG_REV.ModelDescription":0 }, "Node13":{ "models.codon.MG_REV.ModelDescription":0 }, "Node16":{ "models.codon.MG_REV.ModelDescription":0 }, "Node19":{ "models.codon.MG_REV.ModelDescription":0 }, "Node2":{ "models.codon.MG_REV.ModelDescription":0 }, "Node20":{ "models.codon.MG_REV.ModelDescription":0.001732564301319626 }, "Node23":{ "models.codon.MG_REV.ModelDescription":0 }, "Node24":{ "models.codon.MG_REV.ModelDescription":0 }, "Node27":{ "models.codon.MG_REV.ModelDescription":0 }, "Node28":{ "models.codon.MG_REV.ModelDescription":0 }, "Node29":{ 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} }, "settings":{ "burn-in":10000, "data-type":"codon", "max-parents":1, "min-subs":1, "samples":100, "steps":100000, "threshold":0.5 }, "tested":0, "timers":{ "Baseline fit":{ "order":1, "timer":4 }, "Network inference":{ "order":2, "timer":0 }, "Overall":{ "order":0, "timer":4 } } }