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From Genomes to Phenotypes: Traitar, the Microbial Trait Analyzer.

Aaron Weimann1, Kyra Mooren2, Jeremy Frank3

  • 1Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany; German Center for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Braunschweig, Germany; Department for Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany.

Msystems
|January 10, 2017
PubMed
Summary
This summary is machine-generated.

Traitar is a new software tool that predicts bacterial traits from genome sequences, aiding in understanding microbial communities and reducing lab work. This microbial trait analyzer helps researchers identify key bacterial capabilities efficiently.

Keywords:
ancestral trait reconstructiongenotype-phenotype inferencemetagenomicsmicrobial traitsphenotypesphyletic patternssingle-cell genomicssupport vector machines

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • The rapid increase in sequenced microbial genomes necessitates advanced interpretation tools.
  • Bacterial traits are crucial for understanding microbial communities, but most remain uncharacterized.
  • Phenotypic characterization of bacteria through traditional lab methods is labor-intensive.

Purpose of the Study:

  • To develop an automated software package, Traitar, for predicting microbial phenotypes directly from genome sequences.
  • To provide a comprehensive set of phenotype classifiers for diverse bacterial traits.
  • To associate predicted phenotypes with specific protein families and their evolutionary histories.

Main Methods:

  • Utilized L1-regularized L2-loss support vector machines for phenotype prediction.
  • Employed phyletic patterns of protein families and their evolutionary histories across microbial species.
  • Validated Traitar on a diverse dataset of 572 bacterial species, including incomplete and draft genomes.

Main Results:

  • Traitar accurately predicts 67 bacterial traits, including substrate utilization, oxygen requirements, morphology, and enzymatic activities.
  • The software successfully identified protein families linked to specific phenotypes.
  • Demonstrated reliable performance on various genome qualities and applicability in metagenomic analysis, aiding metabolic reconstruction.

Conclusions:

  • Traitar offers a robust, automated solution for deriving microbial phenotypes from genome sequences.
  • The software significantly reduces the need for extensive laboratory experiments in bacterial trait discovery.
  • Traitar is a valuable tool for microbiological research, applicable to large-scale genome studies and metagenomics.