Fig. 2.

Accuracy results for the simulated experiment. In this experiment, SASC scores better than any other tool in these measures. Once again SiFit is the poorest scoring method. The accuracy of SPhyR lowers when mutation losses are included into the dataset and it is forced to employ a Dollo model. To the contrary, SASC performs the best when it utilizes the full extent of its capabilities, i.e. the handling of heterogeneous false-negative rates and mutation losses. Notice that larger values in both measures are better