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Table 2 Trained rate matrix and equilibrium distribution

From: TMRS: an algorithm for computing the time to the most recent substitution event from a multiple alignment column

Substitution typeParameterRate
\(\text {A}\leftarrow \text {C}\), \(\text {T}\leftarrow \text {G}\)\(\alpha\)0.16
\(\text {A}\leftarrow \text {G}\), \(\text {T}\leftarrow \text {C}\)\(\beta\)0.57
\(\text {A}\leftarrow \text {T}\), \(\text {T}\leftarrow \text {A}\)\(\gamma\)0.20
\(\text {C}\leftarrow \text {G}\), \(\text {G}\leftarrow \text {C}\)\(\delta\)0.24
\(\text {C}\leftarrow \text {T}\), \(\text {G}\leftarrow \text {A}\)\(\epsilon\)0.59
\(\text {G}\leftarrow \text {T}\), \(\text {C}\leftarrow \text {A}\)\(\eta\)0.25
NucleotideEquilibrium frequency \(\pi\)
A, T0.23
C, G0.27
  1. The elements of rate matrix \(R_{\text {Symmetric}}\) and its equilibrium frequency \(\pi\) are shown. Parameter variables correspond to matrix \(R_{\text {Symmetric}}\) in Table 1. We averaged the parameters optimized using 100,000 sampled alignment columns in the 4d sites. Due to the symmetry of rate matrix, complementary nucleotides have the same equilibrium frequency