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Fig. 2 | Genome Biology

Fig. 2

From: SeATAC: a tool for exploring the chromatin landscape and the role of pioneer factors

Fig. 2

SeATAC detects differential V-plots. a A full ATAC-seq dataset is down-sampled to two separate datasets (dataset #1 and dataset #2) that includes 10% of the sequencing reads. Every read in dataset #2 is shifted to the 3′ direction by a pre-specified distance (e.g., 100 bp) to generate a new dataset #3. The dataset #1 and dataset #2 have the identical V-plot for any genomic regions, while dataset #1 and dataset #3 have different V-plots. b Different tools are used to compare dataset #1 vs. dataset #2 and dataset #1 vs. dataset #3 to detect differential V-plots. The true positive (TP), false positive (FP), true negative (TN), and false negative (FN) predictions are computed. The receiver operating characteristic (ROC) curve is used to evaluate the performance of different tools. c The ROC curves for SeATAC, NucleoATAC, and MACS2 with a shift size of 50 bp. d The violin plot shows the AUC (area under ROC) of SeATAC, NucleoATAC, and MACS2 on 523 ATAC-seq samples from 20 studies. ***Wilcoxon rank sum test p-value < 0.001. e The AUC of SeATAC, NucleoATAC, and MACS2 at different read counts cutoff from 1 to 20 (the minimum reads in a V-plot)

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