RefSeq database growth influences the accuracy of k-mer-based species identification

Accurate species-level taxonomic classification and profiling of complex microbial communities remains a challenge due to homologous regions shared among closely related species and a sparse representation of non-human associated microbes in the database. Although the database undoubtedly has a strong influence on the sensitivity of taxonomic classifiers and profilers, to date, no study has carefully explored this topic on historical RefSeq releases and explored its impact on accuracy. In this study, we examined the influence of the database, over time, on k-mer based sequence classification and profiling. We present three major findings: (i) database growth over time resulted in more classified reads, but fewer species-level classifications and more species-level misclassifications; (ii) Bayesian re-estimation of abundance helped to recover species-level classifications when the exact target strain was present; and (iii) Bayesian reestimation struggled when the database lacked the target strain, resulting in a notable decrease in accuracy. In summary, our findings suggest that the growth of RefSeq over time has strongly influenced the accuracy of k-mer based classification and profiling methods, resulting in different classification results depending on the particular database used. These results suggest a need for new algorithms specially adapted for large genome collections and better measures of classification uncertainty.