## 4 Results and Discussion  
In both the R package as well as the QIIME 2 plugin, we modified the original SRS algorithm by specifying a seed that initializes the random process (_set.seed_) in cases where the SRS uses random subsampling without replacement of the lowest C\({}_{\text{frac}}\). The random step in SRS is rare and negligible for complex microbiome data, as noted previously [5]. Figure 1: User interface of the Shiny app for SRS (\(SRS\)._shiny.app_-function of the ‘SRS’ R package version 0.2.2). (**A**) Rug plot showing the distribution of the number of counts per sample, discarded samples, and summary statistics; (**B**) plot showing SRS curves; (**C**) interactive table with sample names, the number of counts per sample, and summary statistics for the diversity metric. This rather minor modification, however, ensures the reproducibility of SRS, which is essential for microbiome analysis [8]. As an example of microbiome count data normalization using SRS, we utilized a bacterial 165 rRNA gene amplicon sequencing dataset comprising 494 samples derived from an ongoing oral microbiome study. The dataset was processed in QIIME 2 [9]. After anonymization of samples and ASVs, an ASV table comprising a random subset of 100 samples was analyzed. The visualization of SRS curves revealed that the observed ASVs did not decay steadily with decreasing number of reads (Figure 2A). This is due to the way the ranked fractional values (C\({}_{\text{frac}}\)) are chosen: depending on the scaling factor, an ASV with an integer value (C\({}_{\text{int}}\)) of zero may or may not be chosen by ranked subsampling due to its C\({}_{\text{frac}}\), causing a reproducible zigzag behaviour in the observed number of species. The magnitude of the zigzag observed in SRS curves depends on the data structure (balance between rare and abundant ASVs). Despite the zigzag behaviour, the observed ASV richness was frequently observed to be higher after SRS as compared to rarefying (Figure 2B). Therefore, we recommend the use of the SRS Shiny app (_SRS.shiny.app_-function) prior to SRS for the determination of C\({}_{\text{min}}\) for users working in the R environment. QIIME 2 users are also encouraged to utilize.qza files in the SRS Shiny app ([[https://vitorheidrich.shinyapps.io/srsshinyapp/](https://vitorheidrich.shinyapps.io/srsshinyapp/)]([https://vitorheidrich.shinyapps.io/srsshinyapp/](https://vitorheidrich.shinyapps.io/srsshinyapp/)) (accessed on 1 November 2021)). Figure 2: (**A**) SRS curves and (**B**) comparison of SRS curves and repeated rarefying (10 repeats) using the “_richness_” metric (_SRS curve_-function of the ‘SRS’ R package version 0.2.2). The vertical black solid line indicates the chosen number of counts (10,000) to which all samples will be normalized (C\({}_{\text{min}}\)). Since its implementation in accessible platforms, SRS has been used to normalize several microbiome datasets obtained from different environments such as animal guts [10], soils [11], oceans [12], and laboratory cultures [13]. [PERSON] and [PERSON] [4] clearly demonstrated that rarefying should not be used to normalize microbiome count data; thus, we suggest that future studies should compare SRS to commonly used normalization techniques other than rarefying. Conceptualization, V.H., P.K. and L.B. ; methodology, V.H., P.K. and L.B. ; software, V.H. and L.B. ; validation, V.H., P.K. and L.B. ; formal analysis, V.H. and L.B. ; investigation, V.H., P.K. and L.B. ; resources, V.H., P.K. and L.B. ; data curation, V.H. and L.B. ; writing--original draft preparation, V.H. and L.B. ; writing--review and editing, P.K. ; visualization, V.H. and L.B. ; supervision, P.K. ; project administration, V.H., P.K. and L.B. ; funding acquisition, V.H., P.K. and L.B. All authors have read and agreed to the published version of the manuscript. This work was supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the Bonares-SIGNAL project (funding codes: 031A562A and 031B0510A). V.H.