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Table 4 The robustness of target identification across samples of different GC-contents

From: On an enhancement of RNA probing data using information theory

  GC-content \(n=100\) \(n=200\) \(n=300\)
\({\mathbb {P}}(s\in \Omega ^*)\) GC-rich \(0.778 \pm 0.172\) \(0.732 \pm 0.196\) \(0.735 \pm 0.195\)
Uniform \(0.768 \pm 0.178\) \(0.742 \pm 0.192\) \(0.751 \pm 0.187\)
AU-rich \(0.773 \pm 0.176\) \(0.735 \pm 0.195\) \(0.749 \pm 0.188\)
\({\mathbb {P}}(s^*=s)\) GC-rich \(0.720 \pm 0.202\) \(0.655 \pm 0.226 \) \(0.674 \pm 0.220\)
Uniform \(0.669 \pm 0.222\) \(0.646\pm 0.229\) \(0.706 \pm 0.208\)
AU-rich \(0.677 \pm 0.219 \) \( 0.655 \pm 0.226 \) \( 0.701 \pm 0.210 \)
\({\mathbb {P}}(s^*=s\mid s\in \Omega ^*)\) GC-rich \(0.925 \pm 0.259\) \(0.895\pm 0.309\) \(0.917 \pm 0.299 \)
Uniform \(0.871\pm 0.288\) \(0.871 \pm 0.309\) \(0.940\pm 0.277\)
AU-rich \(0.876\pm 0.283\) \(0.891 \pm 0.308\) \(0.936 \pm 0.280\)
  1. We generate 1000 random sequences of length n with different GC-contents, where GC-rich sequences consist of \(30\%\) Gs, \(30\%\) Cs, \(20\%\) As and \(20\%\) Us; Uniform comprise \(25\%\) Gs, \(25\%\) Cs, \(25\%\) As and \(25\%\) Us; AU-rich contain \(20\%\) Gs, \(20\%\) Cs, \(30\%\) As and \(30\%\) Us. This process can be done by software such as GenRGenS [34]. For each sequence, we then generate (unrestricted) Boltzmann samples of \(N=2^{10}\) structures together with a target structure s. We compute the probabilities of identifying the target utilizing the ensemble tree. We display mean and standard deviation