Thank you for the information. I guess I'll try to stick to the R-level
parallelization whenever possible.
Best,
Oleksii
On Wed, 26 May 2021 at 13:47, Martin Morgan wrote:
> The best way to process large files is in chunks using BamFile(…,
> yieldSize = …) and by using ScanBamParam() to select
On Wed, May 26, 2021 at 5:55 PM Stuart Lee wrote:
> Hi You and Lori,
>
> Are fitted models in scope for ExperimentHub? I thought it was more for
> data. Maybe there should be a ModelHub for developers to include trained
> models from papers in their packages?
>
> @You: if that model has been
Hi You and Lori,
Are fitted models in scope for ExperimentHub? I thought it was more for data.
Maybe there should be a ModelHub for developers to include trained models from
papers in their packages?
@You: if that model has been fitted in R take a look at
https://github.com/tidymodels/butcher
Thanks very much for the explanation, Jim.
Best,
Oleksii
On Wed, 26 May 2021 at 16:28, James W. MacDonald wrote:
> Hi Oleksii,
>
> That function is just a simplification of the negation of overlapsAny:
>
> > getAnywhere("%outside%")
> A single object matching '%outside%' was found
> It was
Hi Oleksii,
That function is just a simplification of the negation of overlapsAny:
> getAnywhere("%outside%")
A single object matching '%outside%' was found
It was found in the following places
package:IRanges
namespace:IRanges
with value
function (query, subject)
!overlapsAny(query,
Dear Bioc team,
%outside% operator from IRanges works as one would expect even if GRanges
objects are supplied as operands:
> a <- as("chr1:100-200", "GRanges")
> b <- as("chr2:150-250", "GRanges")
> IRanges::`%outside%`(a, b)
[1] TRUE
> IRanges::`%outside%`(ranges(a), ranges(b))
[1] FALSE
It
Please consider using Experiment Hub to host the large data file. More
information can be found here:
https://bioconductor.org/packages/devel/bioc/vignettes/AnnotationHub/inst/doc/CreateAHubPackage.html
Cheers,
Lori Shepherd
Bioconductor Core Team
Roswell Park Comprehensive Cancer Center
The best way to process large files is in chunks using BamFile(…, yieldSize =
…) and by using ScanBamParam() to select just the components of the bam files
of interest. The number of cores is basically irrelevant for input -- you'll be
using just one, so choose yieldSize to use modest amounts
Dear Bioc team,
I am compiling a package �m6Aboost� and planning to submit it in the
Bioconductor. This package using a trained machine learning model to identify
the correct m6A signals from the miCLIP2 data set (more detail about this
machine learning model can be found in our paper
Incidentally, I was reflecting on this topic the other day and was
wondering whether BiocParallel could have something like OpenMPParam()
that sets the number of threads to some non-zero value via
omp_set_num_threads(). This would provide a consistent framework through
which users could
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