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Table 1 Accuracy of several decoding methods on simulated data

From: Probabilistic approaches to alignment with tandem repeats

 

Alignment

Repeat

Block

Algorithm

error

sn.

sp.

sn.

sp.

SFF marginalized

3.37%

95.97%

97.78%

43.07%

44.87%

SFF posterior

3.53%

95.86%

97.87%

42.70%

47.37%

SFF block

3.51%

93.09%

98.07%

36.50%

41.67%

SFF block Viterbi

3.91%

93.26%

97.96%

35.77%

40.66%

SFF Viterbi

4.04%

95.29%

97.85%

42.70%

48.95%

TANTAN block

5.05%

61.38%

97.48%

0.00%

0.00%

TANTAN block Viterbi

6.17%

67.86%

96.51%

0.00%

0.00%

SFF marginalized

3.02%

98.93%

99.64%

77.01%

76.17%

SFF posterior

3.42%

98.84%

99.51%

75.91%

80.93%

SFF block

3.21%

97.70%

99.87%

80.66%

94.44%

SFF block Viterbi

3.71%

98.12%

99.85%

81.75%

92.18%

SFF Viterbi

3.94%

98.54%

99.45%

75.55%

83.47%

TANTAN block

3.42%

60.45%

99.90%

0.36%

0.46%

TANTAN block Viterbi

3.83%

61.74%

99.88%

0.00%

0.00%

Context

5.98%

    

Muscle

5.62%

    

3-state posterior

4.41%

    

3-state posterior with masked repeats††

5.03%

99.23%

74.16%

7.66%

7.24%

3-state Viterbi (baseline)

4.78%

    

SFF marginalized

3.63%

96.03%

97.74%

42.70%

43.33%

SFF marginalized

3.36%

95.99%

97.81%

40.88%

43.08%

  1. : method uses the real consensus motifs.
  2. : method uses the real consensus motifs and intervals from the real repeat blocks.
  3. : method uses intervals from the real repeat blocks.
  4. ††: Columns with at least one masked character are considered as repeats.
  5. : Parameters for the three-state submodel were estimated from human-chicken alignment.
  6. : Parameters for SFF submodel were perturbed randomly.