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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(GenomicAlignments)
se <- summarizeOverlaps(features, bam_files) |> futurize()
This vignette demonstrates how to use this approach to parallelize the GenomicAlignments functions.
The GenomicAlignments Bioconductor package provides efficient
representation and manipulation of short genomic alignments. The
summarizeOverlaps() function counts the number of reads that map to
each feature (e.g. gene or exon) from one or more BAM files. When
called with a BamFileList, the work is distributed across BAM files
using bplapply(), which can be parallelized.
The summarizeOverlaps() function counts reads overlapping genomic
features across multiple BAM files:
library(GenomicAlignments)
library(Rsamtools)
bam_files <- BamFileList(c("sample1.bam", "sample2.bam", "sample3.bam"))
features <- GRanges("chr1",
IRanges(start = c(1, 1000, 2000), end = c(500, 1500, 2500))
)
se <- summarizeOverlaps(features, bam_files)
Here summarizeOverlaps() processes BAM files sequentially, but we
can easily make it process them in parallel by piping to futurize():
library(futurize)
se <- summarizeOverlaps(features, bam_files) |> 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 GenomicAlignments functions are supported by futurize():
summarizeOverlaps()