Mercurial > repos > iuc > hyphy_busted
view test-data/bgm-out1.json @ 1:b678fe748dc5 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit 2c7a7ff8a55e7584e84335baa3159a63ce7a590c"
author | iuc |
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date | Wed, 21 Aug 2019 12:22:30 -0400 |
parents | |
children | f8d88dcef64e |
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0.04502281246071182, 3, 2, 1], [25, 30, 0.1123773177282409, 0.107330048901509, 0.2197073666297499, 3, 2, 2], [26, 28, 0.01202310187965289, 0.0100752849569055, 0.0220983868365584, 1, 2, 0], [26, 29, 0.01098659894875614, 0.0106778458846393, 0.02166444483339544, 1, 2, 0], [26, 30, 0.008888531742284057, 0.0220072024775357, 0.03089573421981976, 1, 2, 0], [28, 29, 0.3253931306175987, 0.3786081317863912, 0.70400126240399, 2, 2, 2], [28, 30, 0.007946759742122602, 0.03360599467213372, 0.04155275441425633, 2, 2, 1], [29, 30, 0.01669873349292508, 0.03504162520377113, 0.05174035869669621, 2, 2, 1] ], "headers": [ ["Site 1", "Index of site 1"], ["Site 2", "Index of site 2"], ["P [Site 1 –> Site 2]", "Probability that site 2 is conditionally dependent on site 1"], ["P [Site 2 –> Site 1]", "Probability that site 1 is conditionally dependent on site 2"], ["P [Site 1 <–> Site 2]", "Probability that sites 1 and 2 are not conditionally independent"], ["Site 1 subs", "Substitution counts inferred for Site 1"], ["Site 2 subs", "Substitution counts inferred for Site 2"], ["Shared subs", "Substitutions shared by both sites"] ] }, "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":"1.1" }, "branch attributes":{ "0":{ "Baboon":{ "models.DNA.GTR.ModelDescription":0 }, "Cat":{ "models.DNA.GTR.ModelDescription":0.7933191636859576 }, "Chimp":{ "models.DNA.GTR.ModelDescription":0 }, "Cow":{ "models.DNA.GTR.ModelDescription":0.8094119333244753 }, "Horse":{ "models.DNA.GTR.ModelDescription":0.2769077630163064 }, "Human":{ "models.DNA.GTR.ModelDescription":0 }, "Mouse":{ "models.DNA.GTR.ModelDescription":0.1094921058577506 }, "Node1":{ "models.DNA.GTR.ModelDescription":0.4820782253393885 }, "Node12":{ "models.DNA.GTR.ModelDescription":0 }, "Node2":{ "models.DNA.GTR.ModelDescription":0.08259105925424247 }, "Node3":{ "models.DNA.GTR.ModelDescription":0.2526911684326281 }, "Node8":{ "models.DNA.GTR.ModelDescription":0.06531857233184223 }, "Node9":{ "models.DNA.GTR.ModelDescription":0.03361817454867485 }, "Pig":{ "models.DNA.GTR.ModelDescription":0 }, "Rat":{ "models.DNA.GTR.ModelDescription":0.0888784436278628 }, "RhMonkey":{ "models.DNA.GTR.ModelDescription":0 } }, "attributes":{ "models.DNA.GTR.ModelDescription":{ "attribute type":"branch length", "display order":0 } } }, "data partitions":{ "0":{ "coverage": [ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29] ], "name":"bgm.filter.default" } }, "fits":{ "models.DNA.GTR.ModelDescription":{ "AIC-c":472.7691802214979, "Log Likelihood":-210.2027719289308, "Rate Distributions":null, "display order":0, "estimated parameters":24 } }, "input":{ "datatype":"nucleotide", "file name":"/tmp/tmpSz4m4X/job_working_directory/000/3/working/bgm_input.fa", "number of sequences":10, "number of sites":30, "trees":{ "0":"((((Pig:0.147969,Cow:0.21343)Node3:0.08509899999999999,Horse:0.165787,Cat:0.264806)Node2:0.058611,((RhMonkey:0.002015,Baboon:0.003108)Node9:0.022733,(Human:0.004349,Chimp:0.000799)Node12:0.011873)Node8:0.101856)Node1:0.340802,Rat:0.050958,Mouse:0.09795)" } }, "settings":{ "burn-in":10000, "data-type":"nucleotide", "max-parents":1, "min-subs":1, "samples":100, "steps":100000, "threshold":0.5 }, "tested":0, "timers":{ "Baseline fit":{ "order":1, "timer":0 }, "Network inference":{ "order":2, "timer":1 }, "Overall":{ "order":0, "timer":1 } } }