Fig. 4

Effect of decontamination algorithms on major skin inhabitants and contaminants in a low-biomass skin microbiome dataset. The effect of six bioinformatic decontamination algorithms with tool-specific thresholds was evaluated on three typical skin inhabitants (Corynebacterium, Cutibacterium, and Staphylococcus) and three potential contaminants (Acinetobacter, Comamonas, Pseudomonas). While sample-based decontamination algorithms (frequency filter, Decontam frequency filter) had little effect on the relative abundance of the top 10 genera of the low-biomass skin microbiome dataset, control-based decontamination algorithms (Decontam prevalence filter, SourceTracker, presence filter, MicrobIEM span filter, MicrobIEM ratio filter) specifically reduced Pseudomonas and Comamonas. MicrobIEM’s span filter of “1 of all” is equivalent to the presence filter. Horizontal black lines indicate the relative abundance per genus before applying the bioinformatic decontamination approaches. Freq. = frequency, prev. = prevalence