Figure 1

Creation of a graph and calculation of clustering coefficient from sRNA sequence data. A) sRNAs 1 - 5 are aligned to the target genome. B) The graph is then created, each of the green circles is a vertex that represents a sRNA and an edge (black line) is drawn between them if the sRNAs are close enough to each other on the genome. Each interconnected vertex-island is called a component and, for simplicity a single vertex island is shown. C) For each vertex in each component in the graph, the clustering coefficient is calculated, ie the ratio of the number of edges that are found between neighbours of the vertex (black lines) to the number of edges that could exist between them (red lines are edges that could exist, but do not). For example, vertex 1 connects to vertex 2 and 3. Just one edge could exist between 2 and 3, and one edge does exist, so the clustering coefficient for this node is 1/1, or 1. Similarly, vertex 3 has edges to vertices 1, 2 and 4. Three edges could exist between these three vertices but only one does (between 1 and 2), thus the clustering coefficient for vertex 3 is 1/3. The clustering coefficient of the entire component is the average of the individual clustering coefficients for each node. D) Example patterns of overlap and their corresponding clustering coefficients (c).