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PRAP: Pan Resistome analysis pipeline.

Yichen He1, Xiujuan Zhou1, Ziyan Chen1

  • 1Department of Food Science and Technology, MOST-USDA Joint Research Center for Food Safety, School of Agriculture & Biology, and State Key Lab of Microbial Metabolism, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.

BMC Bioinformatics
|January 17, 2020
PubMed
Summary
This summary is machine-generated.

A new tool, Pan Resistome Analysis Pipeline (PRAP), rapidly identifies antibiotic resistance genes (ARGs) and analyzes their distribution across pathogen populations using a pan-genome approach.

Keywords:
IdentificationMachine learningPan-resistomeVisualization

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Antibiotic resistance genes (ARGs) spread via horizontal gene transfer, leading to varied distribution patterns within species.
  • A pan-genome approach is crucial for understanding ARG distribution in pathogen populations.
  • Existing tools lack integrated ARG identification and pan-genome analysis capabilities.

Purpose of the Study:

  • To develop a tool for integrated analysis of antibiotic resistance genes and pan-genome features.
  • To facilitate the characterization of ARG distribution and potential relationships with phenotypic traits.

Main Methods:

  • Developed the Pan Resistome Analysis Pipeline (PRAP) for rapid ARG identification from whole genome sequences.
  • Utilized CARD and ResFinder databases for ARG identification.
  • Employed detailed annotations for pan-resistome analysis and random forest classification for allele contribution prediction.
  • Applied PRAP to analyze 26 Salmonella enterica isolates.

Main Results:

  • PRAP successfully identified ARGs and analyzed pan-resistome features.
  • The tool provided browsable files and visualization options for results.
  • Demonstrated PRAP's effectiveness on Salmonella enterica isolates.

Conclusions:

  • PRAP is an effective tool for identifying ARGs and visualizing pan-resistome data.
  • Facilitates pan-genomic investigations of ARGs.
  • Enables further exploration of ARG-phenotype relationships.