Robust comparative analysis and contamination removal for metagenomics

With Recentrifuge, researchers can interactively explore what organisms are in their samples and at which level of confidence, enabling robust comparative analysis of multiple samples in any metagenomic study.

  • Removes diverse contaminants, including crossovers, using a novel robust contamination removal algorithm.
  • Provides a confidence level for every result, since the calculated score propagates to all the downstream analysis and comparisons.
  • Unveils the generalities and specificities in the metagenomic samples, thanks to a new comparative analysis engine.

Recentrifuge's novel approach combines robust statistics, arithmetic of scored taxonomic trees, and parallel computational algorithms.

Recentrifuge is especially useful in the case of low microbial biomass metagenomic studies and when a more reliable detection of minority organisms is needed, like in clinical, environmental and forensic analysis. Beyond the standard confidence levels, Recentrifuge implements others devoted to variable length reads, like in the datasets generated by nanopore sequencers.

Recentrifuge is free software available on PyPi and GitHub.

Recentrifuge directly supports Centrifuge, LMAT, CLARK, and Kraken taxonomic classifiers.

Other classification software is supported through a generic interface.

For usage and documentation, please, see the wiki and the user manual.

For details, please, check the article: Martí JM (2019) Recentrifuge: Robust comparative analysis and contamination removal for metagenomics. PLOS Computational Biology 15(4): e1006967.