Benchmarking the impact of reference genome selection on taxonomic profiling accuracy
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Motivation
Over the past decades, genome databases have expanded exponentially, often incorporating highly similar genomes at the same taxonomic level. This redundancy can hinder taxonomic classification, leading to difficulties distinguishing between closely related sequences and increasing computational demands. While some novel taxonomic classification tools address this redundancy by filtering the input genome database, there is limited work exploring the impact of different sequence dereplication methods across taxonomic classification tools.
Results
We assess the effect of genome selection approaches on taxonomic classification using both bacterial and viral datasets. Our results demonstrate that careful selection of reference genomes can improve classification accuracy. We also show that using prior knowledge about a metagenomic sample, such as sampling location, can significantly improve classification accuracy. Finally, we find that using a redundancy-filtered genome database generally reduces the computational resources required, with minimal loss in classification accuracy.
