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bioRxiv · 10.1101/2023.12.07.570547

ganon2: up-to-date and scalable metagenomics analysis

Piro, V. C.·Reinert, K.
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Abstract

The fast growth of public genomic sequence repositories greatly contributes to the success of metagenomics. However, they are growing at a faster pace than the computational resources to use them. This challenges current methods, which struggle to take full advantage of massive and fast data generation. We propose a generational leap in performance and usability with ganon2, a sequence classification method that performs taxonomic binning and profiling for metagenomic analysis. It indexes large datasets with a small memory footprint, maintaining fast, sensitive, and precise classification results. Based on the full NCBI RefSeq and its subsets, ganon2 indices are on average 50% smaller than state-of-the-art methods. Using 16 simulated samples from various studies, including the CAMI 1+2 challenge, ganon2 achieved up to 0.15 higher median F1-Score in taxonomic binning. In profiling, improvements in the F1-Score median are up to 0.35, keeping a balanced L1-norm error in the abundance estimation. ganon2 is one of the fastest tools evaluated and enables the use of larger, more diverse, and up-to-date reference sets in daily microbiome analysis, improving the resolution of results. The code is open-source and available with documentation at https://github.com/pirovc/ganon.

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Piro, V. C., Reinert, K.. 2023-12-08. ganon2: up-to-date and scalable metagenomics analysis. https://doi.org/10.1101/2023.12.07.570547

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