Efficient context-dependent model building based on clustering posterior distributions for non-coding sequences

Table 10 Stepwise context reduction for the nuclear SSU rRNA dataset using the graph-based approach.

Model	Contexts	Annealing	Melting	log BF
GTR16C	16 (96)	[-21.25; -15.74]	[-19.29; -14.31]	-17.65
GTR15C	15 (90)	[-17.17; -13.45]	[-14.19; -10.30]	-13.78
GTR14C	14 (84)	[-14.05; -10.21]	[-10.09; -5.13]	-9.87
GTR13C	13 (78)	[-7.29; -4.07]	[-8.00; -3.25]	-5.65
GTR12C	12 (72)	[-2.61; 1.25]	[-0.71; 3.31]	0.31
GTR11C	11 (66)	[1.75; 5.55]	[6.05; 9.25]	5.65
GTR10C	10 (60)	[3.50; 7.11]	[5.53; 10.37]	6.63
GTR9C	9 (54)	[2.79; 6.32]	[8.24; 12.51]	7.47
GTR8C	8 (48)	[8.43; 12.07]	[10.37; 13.48]	11.09
GTR7C	7 (42)	[4.21; 7.25]	[9.53; 13.04]	8.51
GTR6C	6 (36)	[11.71; 14.99]	[12.83; 17.55]	14.27
GTR5C	5 (30)	[8.97; 12.70]	[10.10; 14.86]	11.66
GTR4C	4 (24)	[11.44; 15.02]	[12.18; 16.35]	13.75
GTR3C	3 (18)	[15.30; 17.92]	[14.86; 18.11]	16.55
GTR2C	2 (12)	[-1.61; 2.74]	[1.04; 5.31]	1.87
GTR	1 (6)	-	-	0

The stepwise context reduction using the graph-based clustering approach reveals an optimal model with 3 clusters for the nuclear SSU rRNA dataset (GTR3C). It attains a log Bayes Factor of 16.55 (as compared to GTR1C), a significant improvement over the full context-dependent model (GTR16C).

ISSN: 2730-7182