Fizzy. Feature subset selection for metagenomics [electronic resource].
- Washington, D.C. : United States. Dept. of Energy. Office of Science, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 8 pages : digital, PDF file
- Additional Creators:
- United States. Department of Energy. Office of Science and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Background: 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. 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. The standalone implementation can be found at http://github.com/EESI/Fizzy.
- Report Numbers:
- E 1.99:1242040
- Published through SciTech Connect.
BMC Bioinformatics 16 1 ISSN 1471-2105 AM
Gregory Ditzler; J. Calvin Morrison; Yemin Lan; Gail L. Rosen.
Kent State Univ., Kent, OH (United States)
- Funding Information:
View MARC record | catkey: 23499970