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Primarily, in vitro parameters may impact the performance of cgMLST reaching high, intermediate and low precision depending on a given targeted read and kmer depth and breadth of coverage (Fig. 5). Using in vitro parameters associated with challenge-specific quality metrics (i.e. number of median absolute differences), each tested workflow was associated with one (prediction). A focus on assembly-based workflow testing was set because we observed that they can produce high and low precision of cgMLST profiles depending on the applied allele caller (Fig. 3: Additional file 5: Fig. S6; Fig. 6: Additional file 7: Fig. S10). For each workflow, for each tested genome and each tested in vitro parameter we calculated a precision score and a quality score, i.e. higher precision scores are associated with lower values of IAAR while higher quality scores are associated with higher values of IAAS (Fig. 5A-Fig. 5D; Additional file 5A-Additional file 5D). Precision scores were not related to any metrics (Fig. 5C), whereas quality scores were significantly associated with several metrics (Fig. 5F; Additional file 5E; Additional file 7F). Higher calculated quality scores were associated with high values of L50, LA50, C1000 and C10000 and low values of L50, LA50, C1000 and C10000 (Fig. 5F). In the 43 tested genome pairs, the default MLST-based workflow resulted in higher quality scores which is also in accordance with higher values of IAAS (Fig. 5D, Fig. 7). Here precision was not significantly related to any metric (Fig. 5B). All other workflows yielded precision values between 1.0e-6 and 1.0e-1 and higher quality scores between 7.5e-1 and 7.5e17 (Fig. 5B-Fig. 5D; Additional file 5B-Additional file 5D). Especially, MLST-based workflows (BioNumerics, DrDk, MentaLiST, SeqSphere and InNuendo) and assembly-free workflows (MiniSeq, SeqSphere, GENPAT) may result in higher quality scores. This emphasizes the impact of tools (Additional file 5E).
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