Recentrifuge

Recentrifuge performs contamination-aware comparative taxonomic analysis of metagenomic sequencing data, removing negative-control and crossover taxa and reporting confidence-weighted taxonomic classifications for downstream comparisons, including high-throughput datasets such as nanopore sequencing.

Key Features:

• Contamination removal: Removes reagent-, laboratory-, and host-derived contaminants by subtracting negative-control taxa and identifying crossover taxa.
• Confidence-weighted classification: Computes and reports confidence scores for taxonomic classifications to support confidence‑aware interpretation of profiles.
• Comparative analysis: Identifies shared and exclusive taxa per sample to enable within- and between-specimen comparisons.
• Algorithm validation: Implements a novel contamination-removal algorithm validated on synthetic datasets with increased specificity while maintaining high sensitivity in the presence of cross-contaminants.
• High-throughput support: Handles large per-sample datasets typical of nanopore sequencing to facilitate scalable comparative analyses.

Scientific Applications:

• Environmental metagenomics: Analyzes environmental microbiomes such as Arctic and Antarctic solar-panel communities to discern taxonomic profiles across distinct ecosystems while accounting for contamination.
• Clinical metagenomics: Processes clinical datasets, including RNA from plasma, to extract taxonomic signals despite heavy contamination and to support detection of blood-associated microbiota translocated from the gut, oral cavity, and genitourinary tract.

Methodology:

Performs subtraction of negative-control taxa and identification of crossover taxa, computes confidence scores for taxonomic classifications, implements a novel contamination‑removal algorithm, and was validated using synthetic datasets demonstrating improved specificity with maintained sensitivity.