Figure 7
From: Generator based approach to analyze mutations in genomic datasets
Time complexity analysis. We compare the running time of our mutation detection method and two common alignment algorithm through the synthetic experiments. We use 4 combinations of different number of sequences in each group N and mutation probability p: (a) \(N=50, p=5\%\); (b) \(N=50, p=10\%\); (c) \(N=100, p=5\%\); (d) \(N=100, p=10\%\). The time complexity of our method is O(N) while the time complexity of the state of the art alignment algorithms is O(\(N^2\)). The biopython package implementation of Smith–Waterman and Needleman-Wunsch algorithms were used in our analysis. As can be seen, the proposed method has clear time complexity advantages for the sequences longer than 4000/nt. The system configuration of the computer on which the experiments were run are: Processor: 2.4 GHz Intel Core i5; Memory: 16 GB 2133 MHz LPDDR3.
