Fig. 3

Benchmarking of decontamination algorithms in mock communities. In the even mock community (A), sample-based decontamination algorithms perform best (frequency filter, Decontam frequency filter); whereas in the staggered mock communities A and B (B, C), control-based decontamination algorithms perform better (Decontam prevalence filter, SourceTracker, presence filter, MicrobIEM span filter, MicrobIEM ratio filter). MicrobIEM’s span filter of “1 of all” is equivalent to the presence filter, and the number of available thresholds for MicrobIEM’s span filter depends on the number of negative controls per dataset (A: 1, B: 3, C: 4 pipeline negative controls). Each algorithm was evaluated by its ability to distinguish expected mock reads from contaminating reads (defined by reads not matching expected sequences), from high (10⁸) to low-biomass samples (10³ bacterial cells). The performance per algorithm was quantified by Youden’s index, ranging from 1 (perfect classification) over 0 (random classification) to − 1 (indicating reversed labels). Algorithms were run separately per dilution, except for the Decontam frequency filter in A and SourceTracker in all datasets. Values in B represent mean values over triplicates per dilution, and values in C represent mean values over four replicates per dilution. Freq. = frequency, prev. = prevalence