When bigger is not better: the impact of RefSeq growth on Kraken2 classification accuracy
TL;DR
This study analyzed the influence of RefSeq database size and composition on taxonomic identification performance using Kraken 2, a widely used taxonomic classification and profiling method and found that resource-efficient Kraken 2 Lite (capped) databases generally exhibit lower classification accuracy compared to full reference databases that require high-performance computing infrastructure.
Abstract
Current technologies allow for the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. In this study, we delve into an evaluation of how variations in the reference database impact classification performance. This aspect assumes paramount significance, particularly when considering the continuous evolution and expansion of the NCBI Reference Sequence database (RefSeq) over time. The aim of this study is to analyze the influence of RefSeq database size and composition on taxonomic identification performance using Kraken 2, a widely used taxonomic classification and profiling method. We found that resource-efficient Kraken 2 Lite (capped) databases, which can be run on standard laptops, generally exhibit lower classification accuracy compared to full reference databases that require high-performance computing infrastructure. The size of the RefSeq database and its growth affect the performance of the k-mer-based algorithms in addition to the computing resources need. We reported that changes in RefSeq reference database over time influenced the accuracy of metagenomic taxonomic classification, and training the classifier with more data, for example, new species, worsen the results especially on the most complex microbial communities.