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Table 7 Stepwise context reduction for the Ancestral Repeats dataset using the graph-based approach.

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

Model Contexts Annealing Melting log BF
GTR16C 16 (96) [623.2; 638.2] [645.5; 661.9] 642.2
GTR15C 15 (90) [658.0; 672.1] [665.0; 682.0] 669.2
GTR14C 14 (84) [651.9; 668.4] [664.3; 678.9] 665.9
GTR13C 13 (78) [664.9; 679.6] [676.4; 693.1] 678.5
GTR12C 12 (72) [673.3; 689.1] [685.3; 701.7] 687.4
GTR11C 11 (66) [682.3; 697.9] [693.5; 710.4] 696.0
GTR10C 10 (60) [677.5; 693.4] [697.5; 710.3] 694.7
GTR9C 9 (56) [693.7; 707.6] [710.4; 724.6] 709.1
GTR8C 8 (48) [699.3; 711.7] [712.4; 727.5] 712.7
GTR7C 7 (42) [686.5; 700.0] [705.1; 719.3] 702.7
GTR6C 6 (36) [650.6; 663.0] [651.2; 664.8] 657.4
GTR5C 5 (30) [641.4; 652.3] [639.2; 649.2] 645.5
GTR 1 (6) - - 0
  1. The stepwise context reduction using our graph-based clustering approach reveals an optimal model with 8 clusters for the Ancestral Repeats dataset (GTR8C). It attains a log Bayes Factor of 712.7 (as compared to GTR1C), a significant improvement over the full context-dependent model (GTR16C) which has twice as many parameters. This model also outperforms the 10-clusters model determined by the likelihood-based clustering approach.