Mercurial > repos > rdvelazquez > hyphy_fubar
diff hyphy_fubar.xml @ 1:fa2236bc5ab2 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit b'bff24ad0eccc4083de076adc029c5c3ac50e2852\n'-dirty
| author | rdvelazquez |
|---|---|
| date | Sun, 18 Nov 2018 22:58:53 -0500 |
| parents | a52fcdeacf3d |
| children | 203597c4875e |
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--- a/hyphy_fubar.xml Tue Jun 12 13:59:32 2018 -0400 +++ b/hyphy_fubar.xml Sun Nov 18 22:58:53 2018 -0500 @@ -10,19 +10,16 @@ <![CDATA[ ln -s '$input_file' fubar_input.fa && ln -s '$input_nhx' fubar_input.nhx && - echo $gencodeid > fubar_params && - echo `pwd`/fubar_input.fa >> fubar_params && - echo `pwd`/fubar_input.nhx >> fubar_params && - echo '$grid_points' >> fubar_params && - echo '$mcmc' >> fubar_params && - echo '$chain_length' >> fubar_params && - echo '$samples' >> fubar_params && - echo '$samples_per_chain' >> fubar_params && - echo '$concentration' >> fubar_params && - export HYPHY=`which HYPHYMP` && - export HYPHY_PATH=`dirname \$HYPHY` && - export HYPHY_LIB=`readlink -f \$HYPHY_PATH/../lib/hyphy` && - cat fubar_params | HYPHYMP LIBPATH=\$HYPHY_LIB \$HYPHY_LIB/TemplateBatchFiles/SelectionAnalyses/FUBAR.bf > '$fubar_log' + echo $gencodeid > tool_params && + echo `pwd`/fubar_input.fa >> tool_params && + echo `pwd`/fubar_input.nhx >> tool_params && + echo '$grid_points' >> tool_params && + echo '$mcmc' >> tool_params && + echo '$chain_length' >> tool_params && + echo '$samples' >> tool_params && + echo '$samples_per_chain' >> tool_params && + echo '$concentration' >> tool_params && + @HYPHY_INVOCATION@ \$HYPHY_LIB/TemplateBatchFiles/SelectionAnalyses/FUBAR.bf > '$fubar_log' ]]> </command> <inputs> @@ -67,5 +64,7 @@ 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. ]]> </help> - <expand macro="citations" /> + <expand macro="citations"> + <citation type="doi">10.1093/molbev/mst030</citation> + </expand> </tool>