Fizzy: feature subset selection for metagenomics

By Gregory Ditzler, J. Morrison, Y. Lan, Gail L. Rosen. 2015

49 citations recorded by the source corpus.

Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α– & β–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. 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. We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.

Subfields: Food Science, Molecular Biology

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In Food Science

  1. Nicola Segata
  2. Edoardo Pasolli
  3. Curtis Huttenhower

In Molecular Biology

  1. Nicola Segata
  2. Casey S. Greene
  3. Edoardo Pasolli
  4. Yifan Peng
  5. Brett K. Beaulieu-Jones
  6. G. Way
  7. Amr M. Alexandari
  8. Duy Tin Truong

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