The goal of sourmashconsumr is to parse, analyze, and visualize the outputs of the sourmash python package. The sourmashconsumr package is still under active development.
You can install the development version of sourmashconsumr from GitHub with:
# install.packages("remotes") remotes::install_github("Arcadia-Science/sourmashconsumr")
Eventually, we hope to release sourmashconsumr on CRAN and to provide a conda-forge package. We’ll update these instructions once we’ve done that.
See the vignette for full instructions on how to run the sourmashconsumr package (coming soon!).
To access the functions in the sourmashconsumr package, you can load it with:
The sourmashconsumr package contains a variety of functions to work with the outputs of the sourmash python package. The table below summarizes which sourmash outputs the sourmashconsumr package operates on and the functions that are available. For a complete list of functions in the sourmashconsumr package, see the documentation.
The sourmashconsumr package follows package developer conventions laid out in https://r-pkgs.org/, and changes can be contributed to the code base using pull requests. For more information on how to contribute, see the developer documentation.
- If you use sourmashconsumr in your work, please cite DOI: 10.57844/arcadia-1896-ke33.
- If you use sourmash in your work, please cite DOI: 10.21105/joss.00027.
If you’d like more information on how sourmash works, please see the following publications:
- For a general background on how sourmash works and examples of how to use it: Large-scale sequence comparisons with sourmash
- For a mathematical description of FracMinHash and a demonstration of the accuracy of sourmash gather: Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers