Taxonomic Classification of Bacteria in Shotgun Metagenomic Samples

Abstract

The aim was to investigate taxonomic classification and removal of human host DNA in the context of a bioinformatic analysis pipeline for screening of pathogens. The examination was carried out using simulated short-read sequenced shotgun metagenomic samples. It was found that a majority of human origin DNA could be separated from bacteria using the K-mer based read classifier Kraken 2 with a custom built human only reference database. Effects on taxonomic classification performance were surveyed for variations in sample composition, parameter settings of the taxonomic classifier and reference database composition. Maintaining both high precision and recall for species level taxonomic classification of metagenomic samples was challenging for limited computational resources. A one-size-fits-all approach to taxonomic classification of any shotgun metagenomic sample would be near impossible with the tested K-mer based classifiers (Kraken 2 and Bracken) and instead specialized pipeline tracks optimized for different expected range of species, sequencing depth and abundance distributions could be a solution.