  Profiling is a process that finds similarities between different RNA
secondary structures by extracting signals from the Boltzmann sampling. The
reproducibility of profiling can be identified by the standard deviation of
number of features among Boltzmann samples. We found a strong relationship
between the frequency of each helix class and its standard deviation of the
frequency upon repeated Boltzmann sampling. We developed a perturbation
technique to predict the stability of these featured helix classes without the
need for repeated Boltzmann sampling, with accuracy between 84% and 94%,
depending on the type of RNA. Our technique only requires 0.2% of the
computation time compared to one profiling process.
