pmotoolsr

📖 To see full documentation of PMO and its associated tools, please see https://plasmogenepi.github.io/PMO_Docs/ — the de facto site for all things PMO.

Toolkit for working with Portable Microhaplotype Objects (PMOs) using R. Version 1.1.0.

pmotoolsr reads, writes, validates, processes, builds, and combines PMO files — the JSON format for storing targeted amplicon (microhaplotype) sequencing data and its associated metadata. It is the R companion to pmotools-python and mirrors much of its functionality.

Installation

# install.packages("remotes")
remotes::install_github("PlasmoGenEpi/pmotoolsr")

Index convention: 0-based on disk, 1-based in R

This is the most important thing to know when working with PMOs in R.

A PMO links its sections together with integer indices (for example, a library_sample_info entry points to its specimen via specimen_id, and a detected microhaplotype points into representative_microhaplotypes via mhaps_target_id). On disk these indices are 0-based (the Python/JSON convention).

When a PMO is read into R with read_pmo() or read_pmo_raw(), every index / *_id field (and panel_targets) is shifted to be 1-based, so it behaves like a normal R index. On write, the shift is reversed automatically. Because both R list indexing and the stored ids are 1-based, you can index directly:

pmo <- read_pmo("my_pmo.json")
lib <- pmo$library_sample_info[[1]]
specimen <- pmo$specimen_info[[ lib$specimen_id ]]   # 1-based, works directly

taxon_id fields are not treated as indices and are never shifted.

Every pmo_* function in this package works in this 1-based space, and any PMO written back out (write_pmo(), write_pmo_raw()) is converted back to 0-based, so files remain interoperable with pmotools-python and other tooling.

Quick start

library(pmotoolsr)

pmo <- read_pmo("my_pmo.json")          # or read_pmo_raw() for a plain list

# explore
pmo_get_specimen_names(pmo)
pmo_count_targets_per_library_sample(pmo)
pmo_count_library_samples_per_target(pmo, collapse_across_runs = TRUE)

# validate against the bundled JSON schema
pmo_validate(pmo)

# subset (returns a new PMO list with all indices remapped)
sub <- pmo_filter_by_target_names(pmo, c("t1", "t5", "t9"))
write_pmo_raw(sub, "subset.json.gz")    # compression inferred from extension

# export flat tables / an allele table / a multi-sheet workbook
pmo_export_specimen_meta_table(pmo)
pmo_extract_alleles_per_sample_table(pmo)
pmo_export_to_excel(pmo, "pmo_tables.xlsx")

Representations

PMOs can be held two ways, and the pmo_* functions accept either:

• R6 object from read_pmo() — the typed PortableMicrohaplotypeObject API.
• Plain nested list from read_pmo_raw() — lighter weight.

Functions that build or subset PMOs (the pmo_*_table_to_pmo() builders, pmo_filter_*, pmo_merge_to_pmo(), pmo_combine_pmos()) return a plain PMO list. Use pmo_list_to_r6() to wrap one back into an R6 object, write_pmo_raw() to write it, or pmo_validate() to check it against the schema.

What’s included

• I/O: read_pmo(), read_pmo_raw(), write_pmo(), write_pmo_raw() (gzip/bz2/xz inferred from the file extension).
• Validation (pmo_checker): pmo_validate(), pmo_validate_jsonschema(), pmo_load_schema(), pmo_required_fields_for_class(). Bundled schemas are validated with jsonvalidate.
• Processing (pmo_processor): name↔︎index lookups, name getters, filtering by specimen / library / target / read count / metadata, counting and aggregation, and allele-frequency extraction.
• Export (pmo_exporter): flat metadata tables, target/panel insert BED extraction, the per-sample allele table (for downstream tools such as dcifer and moire), and multi-sheet Excel export.
• Building (pmo_builder): construct a PMO from tables — pmo_mhap_table_to_pmo(), pmo_panel_info_table_to_pmo(), pmo_specimen_info_table_to_pmo(), pmo_library_sample_info_table_to_pmo(), pmo_read_count_by_stage_table_to_pmo() — then assemble with pmo_merge_to_pmo().
• Combining: pmo_combine_pmos() merges multiple PMOs into one, deduplicating shared entities and remapping every index.

See the function reference (?pmotoolsr / help(package = "pmotoolsr")) for the full list.

For developers

Running the tests

The test suite uses testthat. From the package root:

devtools::test()        # run the test suite
devtools::check()       # full R CMD check (build, tests, docs, examples)

Or from a shell:

R CMD INSTALL --install-tests .
Rscript -e 'testthat::test_local()'

Tests do not run automatically on installation. remotes::install_github() builds and installs the package but does not execute the test suite; run devtools::test() (or devtools::check()) explicitly during development.

Regenerating documentation

Roxygen comments are the source of truth for the man/ pages and NAMESPACE:

devtools::document()

Relationship to pmotools-python

Most functions are ports of their pmotools-python counterparts and the source comments note the originating module (for example, pmo_engine/pmo_processor.py). The two implementations are kept in sync; the main difference is the 0-based ↔︎ 1-based index convention described above.

Citation

If you use the Portable Microhaplotype Object format or its tools, please cite:

Hathaway, N. J., Murie, K., Murphy, M., Simkin, A., Amaya-Romero, J., Hubbard, A., Briggs, J., Aranda-Díaz, A., Early, A. M., Wesolowski, A., Neafsey, D. E., Bailey, J. A., & Greenhouse, B. (2025). The Portable Microhaplotype Object and tools. In bioRxivorg (p. 2025.12.10.693568). bioRxiv. https://doi.org/10.64898/2025.12.10.693568

License

GPL (>= 3).