ResPipe

ResPipe: Metagenomic Pipeline for Taxonomic and Antimicrobial Resistance Profiling

ResPipe performs taxonomic composition inference and antimicrobial resistance (AMR) gene content analysis from shotgun metagenomic sequencing data, evaluating the effects of sequencing depth and normalization on community and resistome profiling.


Key Features:

  • Taxonomic Profiling Stability: Maintains <1% dissimilarity in taxonomic composition at approximately 1 million reads per sample, demonstrating robustness to reduced sequencing depth.
  • AMR Gene Content Analysis: Requires ≥80 million reads per sample to recover full AMR gene family richness; detects additional allelic diversity at ~200 million reads in effluent samples.
  • Normalization Techniques: Adjusts for gene length and incorporates an exogenous Thermus thermophilus DNA spike-in to modify perceived gene abundance distributions and improve resistance profiling accuracy.
  • Comparative Genomic Analysis: Compares shotgun metagenomic data with cultured isolates to assess genomic content recovery across pig caeca, river sediment, and effluent samples.

Scientific Applications:

  • Environmental Resistome Characterization: Profiles microbial communities and AMR gene reservoirs in animal gastrointestinal tracts, river sediments, and wastewater effluents using high-depth shotgun metagenomics.

Methodology:

Integrates high-depth shotgun metagenomic sequencing (~200 million reads per sample) from pig caeca, river sediment, and effluent with hybrid sequencing of cultured isolates to evaluate sequencing depth effects, AMR gene recovery, allelic diversity, and concordance between metagenomic and isolate-derived genomic content.

Topics

Details

License:
GPL-3.0
Tool Type:
workflow
Programming Languages:
Python, R
Added:
11/29/2021
Last Updated:
11/24/2024

Operations

Publications

Gweon HS, Shaw LP, Swann J, De Maio N, AbuOun M, Niehus R, Hubbard ATM, Bowes MJ, Bailey MJ, Peto TEA, Hoosdally SJ, Walker AS, Sebra RP, Crook DW, Anjum MF, Read DS, Stoesser N. The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples. Environmental Microbiome. 2019;14(1). doi:10.1186/s40793-019-0347-1. PMID:33902704. PMCID:PMC8204541.

PMID: 33902704
PMCID: PMC8204541
Funding: - Research Councils UK: NE/N019989/1 and NE/N019660/1 - National Institute for Health Research: HPRU-2012-10041

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