Parallelize 'Rsamtools' functions

Henrik Bengtsson

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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!

TL;DR

library(futurize)
plan(multisession)
library(Rsamtools)

bv <- BamViews(bam_files)
counts <- countBam(bv) |> futurize()

Introduction

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().

Example: Counting reads across multiple BAM files in parallel

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)

Supported Functions

The following Rsamtools functions are supported by futurize():

• countBam()
• scanBam()