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. 2014 Dec 2;15:1047. doi: 10.1186/1471-2164-15-1047

Figure 2.

Figure 2

A schematic of the major steps of the DePCRM algorithm. A. Illustration of extended binding peaks from dataset d1, d2 and d3 respectively. B. Illustration of CREs found within each dataset, CREs of the same motif are shown in the same shape and color. C. Construction of CP similarity graph. {P1, P2, P3, P4}, {P5, P6, P7} and {P8, P9, P10} are sets of CPs found in datasets d1, d2 and d3 respectively. For clarity, the CPs formed between motifs in P1 and motifs in P2 and so on in the datasets are not shown. Each CP (represented as a rectangle) is a node of the multi-partied similarity graph, and two nodes are linked by an edge if and only if their S s ≥ β, with S s being the weight, which is not shown for clarity. D. By removing the dotted edges in panel C, MCL cuts the graph into five CP clusters (CPCs): C1 = {P1, P5, P8}; C2 = {P2, P6}, C3 = {P3, P9} , C4 = {P4, P7)} and C5 = {P10}. CPs in a cluster are connected by edges in the same color. The singleton cluster C5 = {P10} is discarded for its low density. E. For each pair Ci and Cj from the four CPCs, we find sets of CPs from the same dataset d k, and compute a co-occurring scores S CPC (Ci, Cj) for the two CPCs. F. Construction of the CPC co-occurring graph using the four CPCs. Cutting the graph using MCL results in two CRMCs, {C1,C2 ,C3} and {C4}. G. After merging motifs into Unique motifs (Umotifs), we project the CREs of CRMCs to the genome and predict the CRMs.