Cubic time algorithms of amalgamating gene trees and building evolutionary scenarios

Biology Direct

Table 4 Example characteristics of the first and second scenario designs

	Artificial data		Biological data
Scenario characteristics	1st design	2nd design	1st design	2nd design
Total cost / expectation	97443.4	151629.7	210917.0	535524.0
Total cost / expectation of gains	60.0	358.4	53448.0	77040.5
Total cost / expectation of losses	38024.0	56660.0	98376.0	187600.5
Total cost / expectation of duplications	26796.0	34324.6	38286.0	44639.6
Total cost / expectation of transfers	32563.4	60168.3	17887.0	223854.8
Total cost / expectation of the gain_big events	0.0	118.4	2920.0	2388.6
Running time	<1m	2m	15m	41m

Input tree data is the same as for Table 3. The tree S is obtained by the supertree building algorithm described in the paper. The degree of ramification k = 10. Individual event costs are as follows: c(dupl)=3, c(loss)=2, c(gain)=12, c(gain_big)=10, c(sleep)=20, c(tr_with)= 17.6, c(tr_without)=19.6. The running time is specified for parallel computations on a 16-CPUs platform. The cost in the second design and the expectation of the total event cost are defined in Table 3 and by formula (6), respectively.

ISSN: 1745-6150