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Table 5 Rate and MSE obtained by running the algorithm proposed in [39] on the M. musculus dataset

From: QualComp: a new lossy compressor for quality scores based on rate distortion theory

LogBinning

R

MSE

 

UniBinning

R

MSE

 

Truncating

R

MSE

60

0

684.29

 

5

0.25

405.14

 

33

0.01

684.29

34

0.29

632.13

 

10

0.35

279.63

 

40

0.26

404.92

26

0.65

80.160

 

17

0.41

226.08

 

50

0.58

137.08

17

0.62

129.42

 

26

0.47

178.60

 

60

1.58

15.01

10

1.13

14.51

 

34

0.51

157.10

 

70

3.24

0.00

5

1.42

6.03

 

60

0.61

118.58

    
    

70

0.67

101.53

    
    

80

0.74

85.92

    
    

90

0.74

85.92

    
    

100

0.79

71.82

    
    

200

1.08

37.53

    
  1. MSE obtained by the LogBinning, UniBinning and Truncating schemes proposed in [39] for different parameters.
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BMC Bioinformatics

ISSN: 1471-2105

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