Fig. 1

Simulation and sub-sampling of differential ChIP-seq experiments. a Schematic overview of simulated peaks and regulation scenarios: Each box represents one test scenario, per scenario the compared samples, and their signal strength are shown in blue and in red. The columns show transcription factor (TF), H3K27ac (sharp mark) and H3K36me3 (broad mark) histone mark ChIP-seq signals (DCSsim width parameters shown below). In the 50:50 regulation scenario, the number of differential regions is equally distributed, while in the 100:0 scenario we assume a global downregulation of the signal. Arrow positions indicate the differential ChIP-seq signals and their color show the sample with the higher signal. b Overview of the benchmarking workflow. We applied DCSsim to simulate in silico data and DCSsub to sub-sample genuine ChIP-seq signals. This resulted in sequence reads for two samples (red and blue). After preprocessing, we directly applied peak-independent tools or peak-dependent DCS tools subsequent to peak calling. The resulting peaks and differential regions are depicted as arrows. To assess the DCS tools, we calculated the area under the precision-recall curves. c Heatmaps and profile plots showing all peak regions of a ChIP-seq experiment for the TF C/EBPa (left), DCSsub sub-sampling from the same dataset (middle), and the DCSsim simulation of TF peak shapes (right). d Quantitative overview of test cases. We generated five independent datasets per peak-regulation scenario. Then we applied three peak callers in combination with 12 peak-dependent DCS tools and 21 peak-independent DCS tools. We used up to 16 parameter setups per DCS tool, and analyses were run for simulated and sub-sampled ChIP-seq data. * HOMER with previously called peaks (HOMERpd)