Table 4

The results of genetic algorithm model selection applied to N = 100 alignments obtained using forward simulations for different strengths of selection (0: neutral, −5: strong purifying selection)

Selection coefficient 2NsΔBICMCCFisher exact test P-valueInferred codon classesSimulated (true) codon classesAmbiguously assigned pairs
Selected
(25)
Neutral (42)
0152.8−0.250.06Selected (45)123210
Neutral (22)1210
−0.5192.60.0930.54Selected (53)21327
Neutral (14)410
−1282.50.514.02e-05Selected (29)19106
Neutral (38)632
−1.5675.00.651.7e-7Selected (26)2064
Neutral (41)536
−21630.40.812.07e-11Selected (23)2123
Neutral (44)440
−517,974.70.972.6e-17Selected (24)2400
Neutral (43)142
Selection coefficient 2NsΔBICMCCFisher exact test P-valueInferred codon classesSimulated (true) codon classesAmbiguously assigned pairs
Selected
(25)
Neutral (42)
0152.8−0.250.06Selected (45)123210
Neutral (22)1210
−0.5192.60.0930.54Selected (53)21327
Neutral (14)410
−1282.50.514.02e-05Selected (29)19106
Neutral (38)632
−1.5675.00.651.7e-7Selected (26)2064
Neutral (41)536
−21630.40.812.07e-11Selected (23)2123
Neutral (44)440
−517,974.70.972.6e-17Selected (24)2400
Neutral (43)142

ΔBIC reports the improvement by the best model found by the genetic algorithm search compared to the baseline MG94 model. The 2 × 2 contingency table of simulated/inferred classifications for each codon pair, the corresponding P-value for association, and the MCC are provided. Bolded values in the 2 × 2 contingency table highlight the number of simulated codon classes that were correctly classified by the genetic algorithm. For classification purposes, we place each codon pair in the selected/neutral class based on which received more model averaged support. To quantify the degree of assignment uncertainty, we also tabulate how many of the 67 rates were “ambiguous,” i.e. had <0.9 support for the class assignment.

Table 4

The results of genetic algorithm model selection applied to N = 100 alignments obtained using forward simulations for different strengths of selection (0: neutral, −5: strong purifying selection)

Selection coefficient 2NsΔBICMCCFisher exact test P-valueInferred codon classesSimulated (true) codon classesAmbiguously assigned pairs
Selected
(25)
Neutral (42)
0152.8−0.250.06Selected (45)123210
Neutral (22)1210
−0.5192.60.0930.54Selected (53)21327
Neutral (14)410
−1282.50.514.02e-05Selected (29)19106
Neutral (38)632
−1.5675.00.651.7e-7Selected (26)2064
Neutral (41)536
−21630.40.812.07e-11Selected (23)2123
Neutral (44)440
−517,974.70.972.6e-17Selected (24)2400
Neutral (43)142
Selection coefficient 2NsΔBICMCCFisher exact test P-valueInferred codon classesSimulated (true) codon classesAmbiguously assigned pairs
Selected
(25)
Neutral (42)
0152.8−0.250.06Selected (45)123210
Neutral (22)1210
−0.5192.60.0930.54Selected (53)21327
Neutral (14)410
−1282.50.514.02e-05Selected (29)19106
Neutral (38)632
−1.5675.00.651.7e-7Selected (26)2064
Neutral (41)536
−21630.40.812.07e-11Selected (23)2123
Neutral (44)440
−517,974.70.972.6e-17Selected (24)2400
Neutral (43)142

ΔBIC reports the improvement by the best model found by the genetic algorithm search compared to the baseline MG94 model. The 2 × 2 contingency table of simulated/inferred classifications for each codon pair, the corresponding P-value for association, and the MCC are provided. Bolded values in the 2 × 2 contingency table highlight the number of simulated codon classes that were correctly classified by the genetic algorithm. For classification purposes, we place each codon pair in the selected/neutral class based on which received more model averaged support. To quantify the degree of assignment uncertainty, we also tabulate how many of the 67 rates were “ambiguous,” i.e. had <0.9 support for the class assignment.

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