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Original data
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Power-law corrected data
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Mapping method
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Median residual (μ ± σ)noise
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Median residual (μ ± σ)signal
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Median signal-to-noise ratio \( \frac{E\left({x}_{signal}^2\right)}{\sigma_{noise}^2} \)
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Median residual (μ ± σ)noise
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Median residual (μ ± σ)signal
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Median signal-to-noise ratio \( \frac{E\left({x}_{signal}^2\right)}{\sigma_{noise}^2} \)
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Bowtie1
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0.018 ± 0.649
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−0.192 ± 2.229
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11.3
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0.002 ± 0.261
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0.006 ± 1.021
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15.4
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Bowtie2 (global)
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0.019 ± 0.642
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−0.169 ± 2.200
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11.3
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0.002 ± 0.244
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0.003 ± 1.022
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17.6
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Novoalign
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0.017 ± 0.641
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−0.153 ± 2.189
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11.3
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0.001 ± 0.238
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−0.001 ± 1.017
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18.2
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BWA
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0.017 ± 0.648
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−0.159 ± 2.193
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11.1
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0.001 ± 0.242
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0.001 ± 1.019
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17.8
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- This table complements the MA-plots in Fig. 6A to D. It summarizes the characteristics of the signal and noise comparisons before and after power-law correction for each aligner across 6 normalization methods. The bias and variance of each normalization method, in terms of signal and noise, are computed from the difference between the comparisons and the fitted noise model and with the summary statistics taken. The signal-to-noise ratio, before and after power-law correction, are also given. The average signal-to-noise ratio improvement is about 1.5 times after the correction
