sraX

sraX performs resistome analysis by detecting and annotating antibiotic resistance genes (ARGs) and validating resistance-associated mutations from assembled FASTA sequences to characterize resistomes and their genomic context.


Key Features:

  • Automated pipeline: Executes a complete resistome analysis workflow on assembled FASTA sequences.
  • Parallel genome analysis: Processes hundreds of bacterial genomes in parallel to detect and annotate putative resistance determinants.
  • Genomic context analysis: Analyzes the arrangement and genomic context of ARGs to provide locus-level insights.
  • Mutation validation: Validates known resistance-conferring mutations reported in reference studies.
  • Drug-class and loci proportion profiling: Illustrates affected drug classes and proportions of different types of mutated loci.
  • Integrated HTML results: Produces a single hyperlinked HTML file that integrates results for navigable presentation.

Scientific Applications:

  • Resistome profiling: Generates detailed ARG inventories and profiles across samples.
  • AMR pattern analysis: Discerns complex antimicrobial resistance patterns across datasets.
  • Mechanism characterization: Supports identification and characterization of resistance mechanisms through mutation validation and genomic context.
  • Comparative and epidemiological surveillance: Compares resistome diversity across samples and aids tracking of resistant strains.
  • Public health insight: Summarizes resistome data to inform strategies addressing antimicrobial resistance.

Methodology:

Reads assembled FASTA sequence files, detects ARGs, annotates them within genomic context, validates known resistance mutations, and generates integrated HTML reports including illustrations of drug classes and loci proportions.

Topics

Details

License:
GPL-3.0
Programming Languages:
Perl, R, Shell
Added:
1/18/2021
Last Updated:
2/21/2021

Operations

Publications

Panunzi LG. sraX: A Novel Comprehensive Resistome Analysis Tool. Frontiers in Microbiology. 2020;11. doi:10.3389/fmicb.2020.00052. PMID:32117104. PMCID:PMC7025521.

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