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bioconductor-biomformat

This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limi

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biomformat

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 1.40.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Imports: jsonlite, Matrix
  • Install: BiocManager::install("biomformat")

When to Use

  • Reading BIOM format files (JSON or HDF5) into R using read_biom().
  • Extracting core observation data (OTU tables) as sparse matrices using biom_data().
  • Converting BIOM objects to SummarizedExperiment objects using biom_to_SummarizedExperiment().
  • Constructing a BIOM object from standard R matrices and data frames using make_biom().
  • Converting BIOM data into a tidy long-format data frame for downstream analysis using as_tibble.biom().

When NOT to Use

  • For statistical analysis of microbiome data, use phyloseq or DESeq2 because biomformat is strictly an I/O and utility package.
  • For raw sequence processing, use dada2 because biomformat operates on already-constructed contingency tables.

Data Requirements

  • BIOM format files (v1 JSON or v2 HDF5).
  • For manual construction via make_biom(), a count matrix (features in rows, samples in columns) and optional data.frame objects for observation and sample metadata.

Key Parameters

  • data (default): The count matrix when using make_biom().
  • observation_metadata (default): Taxonomy or feature metadata data.frame when using make_biom().
  • matrix_element_type ("int"): The data type of the matrix elements in make_biom().
  • rows (default): Character vector to subset features directly in biom_data().
  • columns (default): Character vector to subset samples directly in biom_data().

Best Practices

  • Use read_biom() which automatically detects and routes HDF5 files to read_hdf5_biom().
  • Use as_tibble.biom() to convert data into a tidy long-format for downstream dplyr and purrr workflows.
  • For large datasets (>2GB), use write_hdf5_biom() instead of write_biom() to avoid JSON serialization limits.

Common Pitfalls

  • Attempting to print a massive BIOM object streams too much data; fix this by relying on the brief summary printed by default or using biom_data() to inspect subsets.
  • Functions expecting standard matrices fail on sparse matrix classes; fix this by coercing with as(biom_data(x), "matrix").
  • Writing very large tables with write_biom() fails due to JSON string limits; fix this by using write_hdf5_biom() for HDF5 format.

Alternatives

  • phyloseq: Has many more utilities for interacting with and analyzing this kind of data.
  • SummarizedExperiment: The standard Bioconductor container for rectangular feature-by-sample assay data (though biomformat interoperates with it).

Citations

  • Paul J. McMurdie and Joseph N Paulson (2015). biomformat: An interface package for the BIOM file format. R/Bioconductor package version 1.0.0.

References

<!-- biomate-cta -->

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