[Bioperl-l] module Bio::TreeIO

[Andreas Leimbach]

just realized I didn't reply to list earlier, so here are my two cents.

well, if you're willing to go outside Perl, there are plenty of options 
to cluster distances. E.g. R's `hclust` function with many hierarchical 
clustering algorithms (complete, average etc.)
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/hclust.html

For examples with dendrograms (and package `ape`) see:
https://rstudio-pubs-static.s3.amazonaws.com/1876_df0bf890dd54461f98719b461d987c3d.html

Use the R package `amap` for parallellization:
https://cran.r-project.org/web/packages/amap/index.html


BioPython has also most of the distance/clustering functions of R 
implemented:
http://biopython.org/DIST/docs/tutorial/Tutorial.html#htoc234

Nice Python examples, I think, are integrated in pyani from Leighton 
Pritchard:
https://github.com/widdowquinn/pyani

I'm sure there are also Perl packages for clustering out there, just 
don't know about them.

HTH,
Andreas

--
Andreas Leimbach
Universität Münster
Institut für Hygiene
Mendelstr. 7
D-48149 Münster
Germany

Tel.: +49 (0)551 39 33843
E-Mail: aleimba at gwdg.de

On 10.05.2016 20:08, Fields, Christopher J wrote:
> On May 10, 2016, at 10:51 AM, Youensclark, Ken - (kyclark) <kyclark at email.arizona.edu> wrote:
>>
>> On May 10, 2016, at 5:54 AM, lskatz <lskatz at gmail.com> wrote:
>>>
>>> That's what I thought originally too but the R package made it look like
>>> there might be some magic bootstrapping going on with distances alone.  If
>>> there truly is no distance-matrix bootstrapping algorithm then I am left to
>>> my devices and at least I can make use of assess_bootstrap().
>>
>> I'm following this thread with interest not because I have anything to add about the Perl but because I've been using MinHash in an all-vs-all sample comparison application that runs on the stampede cluster at TACC:
>>
>> 	https://github.com/hurwitzlab/stampede-mash
>>
>> I'd love to know what all you're doing with a distance matrix as far as analysis and visualization.
>>
>> Ken
>
> Nice, we’ve been looking into mash as well for the same reasons.  Nothing to report yet unfortunately (we haven’t even started).
>
> chris
>
>
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