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Table 6 Determining the reduction path for the graph-based reduction approach.

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

Step

Context 1

Context 2

Log likelihood difference

Performed

Model

1

CXC

TXC

872.10

YES

GTR15C

2

GXG

TXG

1064.87

YES

GTR14C

3

GXT

TXC

1300.93

YES

GTR13C

4

CXC

GXC

1524.96

YES

GTR12C

5

AXC

GXC

1750.50

NO

 

6

GXC

TXC

1777.24

SKIP

 

7

AXT

CXG

1868.77

YES

GTR11C

8

CXC

GXT

1925.79

SKIP

 

9

CXA

GXT

1928.98

YES

GTR10C

10

GXC

GXT

1940.97

SKIP

 

11

GXA

TXG

1961.01

YES

GTR9C

12

AXC

GXA

1964.73

YES

GTR8C

13

GXA

GXG

1973.80

SKIP

 

14

AXC

CXG

1983.92

YES

GTR7C

15

AXC

TXG

2043.25

SKIP

 

16

CXA

GXC

2047.72

SKIP

 

17

AXC

GXG

2211.16

SKIP

 

18

CXG

TXG

2302.08

SKIP

 

19

CXG

GXG

2341.46

SKIP

 

20

CXG

GXC

2343.32

YES

GTR6C

21

AXC

AXT

2352.48

SKIP

 

22

GXT

TXA

2363.72

YES

GTR5C

...

...

...

...

...

...

  1. The graph-based reduction approach constructs the optimal model (GTR8C in Table 8) for the Ancestral Repeats dataset in 12 iterations (first column). The second and third column show which 2 contexts (or clusters) are proposed for merging; the fourth column shows the difference in log likelihood between the full 16-contexts model and the resulting 15-contexts model should only those 2 contexts given in the second and third column be merged; the fifth column shows the decision on the proposed merge (YES: the merge is performed; NO: the merge is not performed due to a lower cost alternative; SKIP: the merge is already present in the current clustering); the sixth column shows the resulting model when a merge operation is performed.