Commit 063575da authored by Jerome Waldispuhl's avatar Jerome Waldispuhl
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......@@ -124,7 +124,7 @@ I would suggest the following simple sampling method would be useful:\\
- generate many sequences at random with GC content in each of the five bins from 0.1 to 0.9.\\
- find the MFE for each sequence and then bin them according to MFE using 5 bins.\\
- Now measure properties as a function of MFE and GC content and plot frequencies of multibranched loops on this 5 x 5 array of bins. Does this depend on both properties or just on MFE?}
\claimstag We do not apply, neither suggest, that any selection mechanism is at play. On the contrary, we showed with \maternal that it would result in lower structural diversity and most importantly prevent the discovery of multi-branched structures. We also believe that experiences on random sequences (uniformly sampled) in Figure~6 already address these comments.
\claimstag We do not apply or suggest that any selection mechanism is at play. On the contrary, we showed with \maternal that it would result in lower structural diversity and most importantly prevent the discovery of multi-branched structures. We also believe that experiences on random sequences (uniformly sampled) in Figure~6 already address these comments.
\end{response}
......@@ -143,7 +143,7 @@ This algorithm has been routinely implemented by multiple groups that studied RN
\begin{response}{
Section 4.1.3 - GC content is maintained by an algorithm associated with mutation. However, this is more similar to selection than mutation. It would be possible to generate new mutant bases with probability controlled by the target GC (theta in the Tamura matrix later in the paper). Then mutation defines the GC content and selection may cause a bias away from theta. The extent to which the observed GC content moves away from theta is a measure of how strong selection is relative to mutation rate. In population genetics it is often interesting to know whether a property arises as the result of mutation or selection. In this model, mutation and selection are not clearly distinguished.
Another funny thing about the algorithm is the while loop. This means that no sequences outside the GC range are ever born. It would be more natural to create sequences by random mutation (for example by the Tamura matrix) and then assign them zero fitness if they are outside the range, so that they cannot be parents for the next generation. The latter seems more reasonable biologically - natural selection cannot tell if something is fit until it exists.}
\claimstag Here, again this algorithm has been routinely implemented by multiple groups that studied RNA evolution [CITE]. We appreciate the suggestions, but we also want to point out that it does not change our conclusion. Indeed, these remarks address \maternal, which is shown to not be a realistic hypothesis.
\claimstag This comment appears to discuss the properties of \maternal, which is not the hypothesis supported in our study. Nonetheless, we remind that we adapted our algorithm from techniques previously used by other groups \cite{???}.
\end{response}
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