Table 5 |
|||||||||
|
The method of sampling the posterior distribution of the MCMC chain by averaging random accepted models from the steady state was compared to the method of selecting the model with the overall maximum log likelihood. |
|||||||||
|
Order 3 model |
Order 4 model |
||||||||
|
|
|||||||||
|
Org 1 |
Org 2 |
Frag L |
Sampling type |
D3 |
Accuracy |
LL |
D4 |
Accuracy |
LL |
|
|
|||||||||
|
Arthrobacter aurescens TC1 vs. Sinorhizobium meliloti 1021 |
|||||||||
|
|
|||||||||
|
400 |
Steady state sampled |
1.08 |
0.95 |
-1054490.36 |
|||||
|
|
1.09 |
0.94 |
-1040007.41 |
||||||
|
400 |
Maximum log likelihood |
1.02 |
0.94 |
-1055584.16 |
|||||
|
|
|||||||||
|
1000 |
Steady state sampled |
1.95 |
0.97 |
-2648159.80 |
|||||
|
|
2.52 |
0.99 |
-2637429.69 |
||||||
|
1000 |
Maximum log likelihood |
2.12 |
0.98 |
-2645204.57 |
|||||
|
|
|||||||||
|
Lactococcus lactis subsp. cremoris MG1363 vs. Francisella tularensis subsp. holarctica FTA |
|||||||||
|
|
|||||||||
|
400 |
Steady state sampled |
1.08 |
0.90 |
-1045063.72 |
|||||
|
|
1.33 |
0.95 |
-1040811.10 |
||||||
|
400 |
Maximum log likelihood |
1.15 |
0.92 |
-1047966.99 |
|||||
|
|
|||||||||
|
1000 |
Steady state sampled |
2.02 |
0.96 |
-2624742.76 |
|||||
|
|
2.22 |
0.97 |
-2615376.71 |
||||||
|
1000 |
Maximum log likelihood |
2.19 |
0.96 |
-2626080.18 |
|||||
|
|
|||||||||
|
Helicobacter pylori HPAG1 vs. Streptococcus pneumoniae R6 |
|||||||||
|
|
|||||||||
|
400 |
Steady state sampled |
0.93 |
0.96 |
-1059955.55 |
|||||
|
|
1.18 |
0.93 |
-1045561.25 |
||||||
|
400 |
Maximum log likelihood |
0.97 |
0.96 |
-1061298.85 |
|||||
|
|
|||||||||
|
1000 |
Steady state sampled |
1.71 |
0.99 |
-2656860.50 |
|||||
|
|
2.28 |
0.99 |
-2634722.55 |
||||||
|
1000 |
Maximum log likelihood |
1.69 |
0.98 |
-2658488.27 |
|||||
|
|
|||||||||
|
Staphylococcus aureus RF122 vs. Prochlorococcus marinus str. NATL2A |
|||||||||
|
|
|||||||||
|
400 |
Steady state sampled |
0.99 |
0.90 |
-1049716.33 |
|||||
|
|
1.00 |
0.95 |
-1045188.54 |
||||||
|
400 |
Maximum log likelihood |
0.99 |
0.93 |
-1050316.80 |
|||||
|
|
|||||||||
|
1000 |
Steady state sampled |
1.92 |
0.97 |
-2636903.64 |
|||||
|
|
2.21 |
0.97 |
-2624299.41 |
||||||
|
1000 |
Maximum log likelihood |
1.75 |
0.97 |
-2636046.52 |
|||||
|
|
|||||||||
|
Staphylococcus aureus subsp. aureus COL vs. Methanocaldococcus jannaschii DSM 2661 |
|||||||||
|
|
|||||||||
|
400 |
Steady state sampled |
0.96 |
0.95 |
-1037936.55 |
|||||
|
|
1.05 |
0.89 |
-1033285.36 |
||||||
|
400 |
Maximum log likelihood |
0.92 |
0.94 |
-1037505.67 |
|||||
|
|
|||||||||
|
1000 |
Steady state sampled |
1.84 |
0.98 |
||||||
|
|
2.36 |
0.99 |
-2581181.80 |
||||||
|
1000 |
Maximum log likelihood |
1.94 |
0.98 |
-2601394.32 |
|||||
|
|
|||||||||
|
Frag L, Fragment length; LL, Output model log likelihood The resulting accuracy differences were negligible. Accuracy was also compared in 3-mer models vs. 4-mer models. While 4-mer models slightly outperformed 3-mer models on average, a significant run time increase was observed (not shown). NC_identifiers refer to GenBank accession numbers for genomes listed in each trial. |
|||||||||
|
Kislyuk et al. BMC Bioinformatics 2009 10:316 doi:10.1186/1471-2105-10-316 |
|||||||||
