Bioconductor Biomformat

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

• Homepage: https://bioconductor.org/packages/biomformat
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/biomformat/inst/doc/biomformat.html

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use biomformat. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open biomformat on BioMate →