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The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
library(futurize)
plan(multisession)
library(Rsamtools)
bv <- BamViews(bam_files)
counts <- countBam(bv) |> futurize()
This vignette demonstrates how to use this approach to parallelize the Rsamtools functions.
The Rsamtools Bioconductor package provides an interface to
BAM (Binary Alignment Map) files and other high-throughput sequencing
data formats. Functions like countBam() and scanBam() can process
multiple BAM files in parallel when called with a BamViews object,
which distributes work across BAM files using bplapply().
The countBam() function counts the number of records in BAM files.
When called with a BamViews object containing multiple BAM files,
the counting can be parallelized:
library(Rsamtools)
bam_files <- c("sample1.bam", "sample2.bam", "sample3.bam")
bv <- BamViews(bam_files)
counts <- countBam(bv)
Here countBam() processes BAM files sequentially, but we can easily
make it process them in parallel by piping to futurize():
library(futurize)
counts <- countBam(bv) |> futurize()
This will distribute the BAM file processing across the available parallel workers, given that we have set up parallel workers, e.g.
plan(multisession)
The built-in multisession backend parallelizes on your local
computer and works on all operating systems. There are other
parallel backends to choose from, including alternatives to
parallelize locally as well as distributed across remote machines,
e.g.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following Rsamtools functions are supported by futurize():
countBam()scanBam()These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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