Figure 4
IPD benchmarking (A) F-score plot for short-read quantification for SARS-CoV-2 (N = 71), F. nucleatum (N = 17) and HPV (N = 88) truth set, (B) correlation matrix of the normalized qPCR load of F. nucleatum with the quantification using IPD, PathoScope2, Kraken2 and GATK-PathSeq. Negative correlation between the qPCR data and the quantification by all the tools has been multiplied with minus one for representation of (C) F1-score, sensitivity and precision of different variant calling tools/pipelines on the SARS-CoV-2–simulated short-read dataset (N = 36), (D) accuracy of SARS-CoV-2 lineage prediction by IPD SARS-CoV-2 module, based on the variants derived from short- and long-read samples (N = 53). The X-axis denotes the random background mutation rate introduced in the simulated dataset.

IPD benchmarking (A) F-score plot for short-read quantification for SARS-CoV-2 (N = 71), F. nucleatum (N = 17) and HPV (N = 88) truth set, (B) correlation matrix of the normalized qPCR load of F. nucleatum with the quantification using IPD, PathoScope2, Kraken2 and GATK-PathSeq. Negative correlation between the qPCR data and the quantification by all the tools has been multiplied with minus one for representation of (C) F1-score, sensitivity and precision of different variant calling tools/pipelines on the SARS-CoV-2–simulated short-read dataset (N = 36), (D) accuracy of SARS-CoV-2 lineage prediction by IPD SARS-CoV-2 module, based on the variants derived from short- and long-read samples (N = 53). The X-axis denotes the random background mutation rate introduced in the simulated dataset.

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