On 22.01.2016 21:19, Francesco Cicconardi wrote:
> Thanks Alexis,
>
> So, let's see if options are right:
>
> 1. just run a couple of rapid bootstraps per job (without search for
> the
> best tree) and then concatenate the BS tree files
>
>
> option: "-f a"
yes but make sure to provide different random number seeds
> 2. then also run a couple of standard ML searches independently and
> check the RAxML_info files for the best scoring tree
>
>
> option: "-f d" ???
yes, you can also omit -f d since this is the default anyway
> 3. then you can build consensus trees or draw BS support values on the
> best-scoring tree with the respective RAxML options
>
>
> option: "-f b"
yes, that's for drawing BS support onto the best-scoring ML tree, if you
want to build a consensus out of the BS replicates use the -J option ...
alexis
>
> Alternatively you can deploy the RAxML hybrid MPI-Pthreads version.
>
> Finally, if the dataset is really huge you may consider using ExaML
> instead.
>
>
> I'll check this anyway! ;)
>
> Thanks
> F
>
>
> Alexis
>
> On 19.12.2015 18:57, Francesco Cicconardi wrote:
> > Hi everyone,
> >
> > I'm currently running a rapid Bootstrap analysis and best-scoring ML
> > tree (-f a) on my cluster, it is taking a lot of time and I'll
> probably
> > exceed job's run time limit. Is there a way to resume the run?
> >
> > thank yoy,
> > Francesco
> >
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> --
> Alexandros (Alexis) Stamatakis
>
> Research Group Leader, Heidelberg Institute for Theoretical Studies
> Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
> Adjunct Professor, Dept. of Ecology and Evolutionary Biology,
> University
> of Arizona at Tucson
>
>
www.exelixis-lab.org <
http://www.exelixis-lab.org>