RefSeq database growth influences the accuracy of k-mer-based lowest common ancestor species identification
2018
Daniel J. Nasko | Sergey Koren | Adam M. Phillippy | Todd J. Treangen
Abstract In order to determine the role of the database in taxonomic sequence classification, we examine the influence of the database over time on k-mer-based lowest common ancestor taxonomic classification. We present three major findings: the number of new species added to the NCBI RefSeq database greatly outpaces the number of new genera; as a result, more reads are classified with newer database versions, but fewer are classified at the species level; and Bayesian-based re-estimation mitigates this effect but struggles with novel genomes. These results suggest a need for new classification approaches specially adapted for large databases.
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