Fizzy: feature subset selection for metagenomics

Background: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using alpha- & beta-diversity. Feature subset selection -a sub-field of machine learning -can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome. Results: We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. Conclusions: We have made the software implementation of Fizzy available to the public under the GNU GPL license.