Fig. 4

Differential analysis of MOCCS profiles between ChIP-seq sample pairs can detect differentially recognized k-mers. A Schematic overview of the simulation of differential k-mer detection. B Simulation results of differential k-mer detection. Scatter plot showing MOCCS2scores of all 6-mers in the two simulated ChIP-seq samples. The red and gray points represent the differential k-mers (q < 0.05) and other k-mers, respectively. C Bar plots showing the sensitivity, specificity, and false discovery rate (FDR) of differential k-mer detection under different simulation conditions (Fig. S8B). α is the percentage of input sequences (ChIP-seq peak regions) containing embedded “true significant k-mers,” N is the number of peaks in a ChIP-seq sample, and σ is the standard deviation of the embedded “true significant k-mers” from the center of the peak. D Scatter plots of MOCCS2scores showing differential k-mers between two ChIP-seq samples with the same (left) or different (right) cell types for the same TF (JUN). The red and blue points represent the differential k-mers (q < 0.05) and other k-mers, respectively. E Scatter plots of MOCCS2scores showing differential k-mers between ChIP-seq sample pairs of different TFs in the same cell types (K-562). The pair JUN and FOS (left) represents cofactor-effector pairs, whereas the pair JUN and CTCF (right) represents non-cofactor-effector pairs. The red and blue points represent differential k-mers (q < 0.05) and other k-mers, respectively. The PWM-supported differential k-mers and known PWM motifs (JASPAR) were compared between JUN and CTCF ChIP-seq samples