Improved Metagenomic Analysis for All-Food-Sequencing with AFS-MetaCache2: Illumina vs. Nanopore

Abstract

BackgroundAll-Food-Sequencing (AFS) is a method for untargeted metagenomic analysis that allows for the detection and quantification of food ingredients. While this approach avoids some of the shortcomings of targeted PCR-based methods, its performance depends on sequencing technologies, taxonomic classification tools, and genomic reference databases. ResultsAFS-MetaCache2 implements an improved reference database construction mechanism compared to prior approaches. To demonstrate the effectiveness to AFS, we sequenced sausages composed of mammalian and avian species using both short-read (Illumina) and long-read (Oxford Nanopore Technologies) platforms. While both approaches reliably detect the main components, our comparison shows that long-read sequencing is superior in terms of both quantification accuracy and false positive rates. The evaluation of representative metagenomic tools (Kraken2+Bracken, KrakenUniq, AFS-MetaCache1) demonstrates that AFS-MetaCache2 yields the best accuracy and fastest database build times, while reducing peak main memory consumption. It thus allows for efficient scaling to large reference genome sets. ConclusionOur study suggests that deep sequencing of total genomic DNA from samples with heterogeneous taxon composition, using 3rd generation sequencing technology followed by metagenomic analysis with AFS-MetaCache2, is a valuable approach for bio-surveillance of food ingredients. Our software is available at https://github.com/muellan/metacache.