Convert a pangenome into a metapangenome.
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A metapangenome contains both the information in a metagenome (i.e. their abundances in different samples as described in your profile-db) and the information in a pangenome (i.e. the gene clusters in your dataset). This is useful because you are able to observe which gene cluster patterns are present in certain environments. For an example of a metapangenomic workflow, take a look here (this tutorial was written before this program, but the insights persist).
However, when integrating metagenomic and pangenomic data together, you’ll get a lot of data. You can set two additional parameters to help you filter out data that doesn’t mean certain standards:
--fraction-of-median-coverage: this threshold removes genes with less than this fraction of the median coverage. The default is 0.25. So, for example, if the median coverage in your data was 100X, this would remove all genes with coverage less than 25X.
--min-detection: this threshold removes genomes with detection less than this value in all samples. The default is 0.5.
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Are you aware of resources that may help users better understand the utility of this program? Please feel free to edit this file on GitHub. If you are not sure how to do that, find the
__resources__ tag in this file to see an example.