Abstract
Background: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using aα- & 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.
Original language | English (US) |
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Article number | 358 |
Journal | BMC Bioinformatics |
Volume | 16 |
Issue number | 1 |
DOIs | |
State | Published - Nov 4 2015 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Structural Biology
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics