In this section we want to demonstrate how fast the implementation of our simple algorithm is. We compare the runtime to that of Cliquer [8]. For the test we use both artificial as well as biological data sets. The artificial datasets were generated in the following way: For a given interval of some maximal length ℓmax we computed all possible sub-intervals within this interval. From this set of possible sub-intervals, a given percentage number of sub-intervals was chosen uniformly at random for the further evaluations. Note that the number of possible sub-intervals in an interval with length ℓ is roughly ℓ2, and thus the number of maximal cliques is O(ℓ6). We chose two values for ℓmax, namely 20 and 100. Then, the overall runtime to calculate all maximal cliques was determined for all constraints within 0.05 ≤ c ≤ 0.95 in steps of 0.05. For Cliquer we could only use an interval of length ℓmax = 20 and a randomly chosen set of 30% of all possible sub-intervals. For other test sets Cliquer had runtimes longer than many hours or even days. For ℓmax = 20 and c = 0.05 Cliquer took nearly three hours to compute all maximal cliques, for c = 0.1 it took nearly one hour to compute all maximal cliques, for c = 0.15, c = 0.2, c = 0.25 it took approximately 8, 4, and 1 minutes, respectively. For all c ≥ 0.3 it took under a minute to compute all maximal cliques and it took below 0.01 seconds for all c ≥ 0.5. Our implementation needed no more than 50 ms for any of the c-values.