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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
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Fizzy: feature subset selection for metagenomics.

Gregory Ditzler1, J Calvin Morrison2, Yemin Lan3

  • 1Department of Electrical & Computer Engineering, The University of Arizona, 1230 E Speedway Blvd., ECE Bldg., Tucson, 85721, AZ, USA. gregory.ditzler@gmail.com.

BMC Bioinformatics
|November 6, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces Fizzy, a new Python tool for microbial ecologists. Fizzy uses information-theoretic feature selection to identify key operational taxonomic units (OTUs) that differentiate microbial communities.

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Area of Science:

  • Microbial Ecology
  • Bioinformatics
  • Machine Learning

Background:

  • Comparative metagenomics tools often focus on alpha and beta diversity for exploring bacterial communities.
  • Feature subset selection, a machine learning technique, offers novel insights into metagenomic and 16S data.
  • Identifying influential operational taxonomic units (OTUs) or functional features is crucial for understanding biological conditions.

Purpose of the Study:

  • To develop a user-friendly software tool for microbial ecologists.
  • To implement information-theoretic feature selection methods for biological data analysis.
  • To facilitate the identification of key microbial features differentiating between conditions.

Main Methods:

  • Development of a Python command-line tool named Fizzy.
  • Implementation of information-theoretic feature selection algorithms.
  • Ensuring compatibility with the widely adopted BIOM data format.

Main Results:

  • Fizzy successfully implements information-theoretic subset selection for microbial ecology data.
  • The tool's capabilities were demonstrated using publicly available datasets.
  • The software aids in identifying influential OTUs or functional features.

Conclusions:

  • The software implementation, Fizzy, is publicly available under the GNU GPL license.
  • Fizzy provides a valuable resource for microbial ecologists.
  • The tool is accessible via GitHub for standalone implementation.