Commit bff4e292 authored by Jerome Waldispuhl's avatar Jerome Waldispuhl
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@article{Higgs:2015aa,
Abstract = {The RNA World concept posits that there was a period of time in primitive Earth's history - about 4 billion years ago - when the primary living substance was RNA or something chemically similar. In the past 50 years, this idea has gone from speculation to a prevailing idea. In this Review, we summarize the key logic behind the RNA World and describe some of the most important recent advances that have been made to support and expand this logic. We also discuss the ways in which molecular cooperation involving RNAs would facilitate the emergence and early evolution of life. The immediate future of RNA World research should be a very dynamic one.},
Author = {Higgs, Paul G and Lehman, Niles},
Date-Added = {2017-08-15 05:04:50 +0000},
Date-Modified = {2017-08-15 05:04:50 +0000},
Doi = {10.1038/nrg3841},
Journal = {Nat Rev Genet},
Journal-Full = {Nature reviews. Genetics},
Mesh = {Base Pairing; Evolution, Molecular; Models, Biological; Origin of Life; RNA; RNA, Catalytic},
Month = {Jan},
Number = {1},
Pages = {7-17},
Pmid = {25385129},
Pst = {ppublish},
Title = {The {RNA World:} molecular cooperation at the origins of life},
Volume = {16},
Year = {2015},
Bdsk-Url-1 = {http://dx.doi.org/10.1038/nrg3841}}
@article{Waldispuhl:2002aa,
Abstract = {MOTIVATION: S-attributed grammars (a generalization of classical Context-Free grammars) provide a versatile formalism for sequence analysis which allows to express long range constraints: the RNA folding problem is a typical example of application. Efficient algorithms have been developed to solve problems expressed with these tools, which generally compute the optimal attribute of the sequence w.r.t. the grammar. However, it is often more meaningful and/or interesting from the biological point of view to consider almost optimal attributes as well as approximate sequences; we thus need more flexible and powerful algorithms able to perform these generalized analyses.
RESULTS: In this paper we present a basic algorithm which, given a grammar G and a sequence omega, computes the optimal attribute for all (approximate) strings omega(') in L(G) such that d(omega, omega(')) < or = M, and whose complexity is O(n(r + 1)) in time and O(n(2)) in space (r is the maximal length of the right-hand side of any production of G). We will also give some extensions and possible improvements of this algorithm.},
Author = {Waldisp{\"u}hl, J and Behzadi, B and Steyaert, J-M},
Date-Added = {2017-08-15 04:54:49 +0000},
Date-Modified = {2017-08-15 04:54:49 +0000},
Journal = {Bioinformatics},
Journal-Full = {Bioinformatics (Oxford, England)},
Mesh = {Algorithms; Natural Language Processing; Pattern Recognition, Automated; Sequence Alignment; Sequence Analysis, RNA; Sequence Homology, Nucleic Acid},
Pages = {S250-9},
Pmid = {12386010},
Pst = {ppublish},
Title = {An approximate matching algorithm for finding (sub-)optimal sequences in {S}-attributed grammars},
Volume = {18 Suppl 2},
Year = {2002}}
@article{Fontana:1998aa,
Abstract = {Understanding which phenotypes are accessible from which genotypes is fundamental for understanding the evolutionary process. This notion of accessibility can be used to define a relation of nearness among phenotypes, independently of their similarity. Because of neutrality, phenotypes denote equivalence classes of genotypes. The definition of neighborhood relations among phenotypes relies, therefore, on the statistics of neighborhood relations among equivalence classes of genotypes in genotype space. The folding of RNA sequence (genotypes) into secondary structures (phenotypes) is an ideal case to implement these concepts. We study the extent to which the folding of RNA sequence induces a "statistical topology" on the set of minimum free energy secondary structures. The resulting nearness relation suggests a notion of "continuous" structure transformation. We can, then rationalize major transitions in evolutionary trajectories at the level of RNA structures by identifying those transformations which are irreducibly discontinuous. This is shown by means of computer simulations. The statistical topology organizing the set of RNA shapes explains why neutral drift in sequence space plays a key role in evolutionary optimization.},
Author = {Fontana, W and Schuster, P},
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......@@ -265,22 +265,23 @@ Why should selection for stability be the only pressure determining the observed
\begin{response}{
Page 12, end of first paragraph. Same objection as previously. Is the quantitative observation really surprising? I miss all across the ms a clear characterization of the "region of the sequence landscape enriched with multi-branched structures". As it stands, the easiest interpretation is that the region corresponds to the most abundant structures at length 50, and this region would not be generic for other parameters (as length or sampling size).}
Voila
We answered above to this remark. The term ``distinct region'' is unfortunate and eventually ambiguous. Our intent was not to describe a connected network or a dense region of sequences, but an ensemble of sequences occurring at a specific GC content regime. In fact, we noted on page 9 of our manuscript that the sequence entropy of mutants with stable multi-branched structures is high, which suggested that these sequences are randomly distributed.
\end{response}
\begin{response}{
Page 14. Two interpretations are given to the presence of these structures, and here finally the authors converge to the simplest (and likely correct) explanation. First, they should know that rod-shaped folds are the most resilient architecture to point mutations (check the literature on viroids). The second option (that they are just observing the typical structure at a given length) is much more plausible also less fancy.}
Voila
\hypothesistag We do not fully understand the emphasis on viroids since as stated above our focus is rather on the emergence of ribozymes. It is also fair to discuss all possible scenarios in the Discussion section starting on page~14.
\end{response}
\begin{response}{
Page 15. I definitely disagree with the conclusion that "Variations of the sizes of populations or lengths of RNA sequences (...) we do not expect any major impact on our conclusions" for all the reasons exposed.}
Voila
\claimstag We believe that there is a profound misunderstanding here, for which we take full responsibility. First, as mentioned above, we consider various population sizes to guarantee robustness of our results. Next, we were only speaking of minor variation of the sequence length corresponding to a couple of insertion or deletion. As the reviewer can note by looking at the citation at the end of the sentence, this remark is mainly to introduce the availability of more sophisticated algorithm modelling indels \cite{Waldispuhl:2002aa}.
\end{response}
\begin{response}{
Summarizing, I guess that the region of structural complexity (according to the definition of the authors) identified in these very large simulations does not depart from typical (understood as abundant) structures for RNA sequences of length 50. If this is so, this study does not contribute any advance with respect to previous works where typical structures have been analytically characterized for all lengths or where it has been demonstrated that non-coding natural RNA structures do belong to abundant folds and are, in this respect, dominated by entropic principles and not functional selection. The results here presented agree with previous findings and I cannot see its relevance in the context of the early stages of life. Therefore, I cannot recommend publication of this work.}
Voila
We regret this poor appreciation of our work by the reviewer, but we sincerely hope that our clarifications will provide him material to reconsider his judgement.\\
Moreover, we would to stress that our results \cite{Briones:2009aa} \cite{Higgs:2015aa}
\end{response}
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