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bioconductor-ompbam

This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged in

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ompBAM

Workflows

Standard Workflow

library(ompBAM)

# Create a new package template that compiles with ompBAM
pkg_path <- file.path(tempdir(), "MyPkg")
use_ompBAM(pkg_path)

# Retrieve the path to an example BAM file provided by the package
bam_path <- ompBAM::example_BAM("Unsorted")

Input: A destination directory path for the new package.
Output: A fully configured R package directory structure containing C++ templates and configuration files linked to ompBAM.

When to Use

  • Developing R packages that require high-performance, multi-threaded processing of BAM files using C++ and OpenMP.
  • Writing custom BAM parsing algorithms where you want ompBAM to handle file access, BGZF decompression, and thread management.
  • Creating packages that need to run on unsorted and unindexed BAM files (e.g., using pbam_in and pbam1_t APIs).

When NOT to Use

  • For standard BAM manipulation or filtering in R without writing C++ code; use Rsamtools or GenomicAlignments instead.
  • For querying small genomic regions of sorted and indexed BAM files; use htslib directly as ompBAM is optimized for whole-file sequential reads.

Data Requirements

  • Input BAM files (can be unsorted and unindexed).
  • A package development environment with Rcpp and a C++ compiler supporting OpenMP (e.g., gcc or clang with OpenMP libraries).

Key Parameters

  • path (in use_ompBAM): The directory path where the new package project will be created.
  • bam_file: Path to the BAM file to be processed.
  • n_threads: Number of threads to use for parallel execution.

Best Practices

  • Run devtools::document() on the newly created package to generate the NAMESPACE and export the R wrapper functions.
  • Use the #ifdef _OPENMP directive in C++ code to ensure compatibility with systems that do not have OpenMP installed (such as default macOS configurations).
  • Call pbam1_t::realize() if you need to store reads in memory across multiple calls of pbam_in::fillReads(), as virtual reads point to temporary buffers.

Common Pitfalls

  • macOS Compilation Failure: macOS does not support OpenMP natively. Fix: Install libomp via Homebrew (brew install libomp) and configure compiler flags.
  • Memory Corruption with Virtual Reads: Accessing pbam1_t objects after calling fillReads() leads to segmentation faults. Fix: Call realize() on the pbam1_t object to copy the data into persistent memory.
  • Requesting Too Many Threads: Requesting more threads than available can degrade performance. Fix: Use a helper function to cap requested threads at omp_get_max_threads().

Alternatives

  • Rsamtools: For high-level BAM file manipulation and querying in R.
  • GenomicAlignments: For representing and manipulating genomic alignments in R.
  • Rcpp: For general C++ integration in R packages without built-in BAM parsing support.

Citations

  • Alex C H Wong (2026). ompBAM API Documentation.

References

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