Commit 54ea07f3 authored by Jerome Waldispuhl's avatar Jerome Waldispuhl
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update appeal bib

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Abstract = {This paper presents a new computer method for folding an RNA molecule that finds a conformation of minimum free energy using published values of stacking and destabilizing energies. It is based on a dynamic programming algorithm from applied mathematics, and is much more efficient, faster, and can fold larger molecules than procedures which have appeared up to now in the biological literature. Its power is demonstrated in the folding of a 459 nucleotide immunoglobulin gamma 1 heavy chain messenger RNA fragment. We go beyond the basic method to show how to incorporate additional information into the algorithm. This includes data on chemical reactivity and enzyme susceptibility. We illustrate this with the folding of two large fragments from the 16S ribosomal RNA of Escherichia coli.},
Author = {Zuker, M and Stiegler, P},
Date-Added = {2017-08-13 23:09:33 +0000},
Date-Modified = {2017-08-13 23:09:33 +0000},
Journal = {Nucleic Acids Res},
Journal-Full = {Nucleic acids research},
Mesh = {Base Composition; Base Sequence; Calorimetry; Computers; Escherichia coli; Humans; Immunoglobulin gamma-Chains; Models, Molecular; Molecular Weight; Nucleic Acid Conformation; RNA; RNA, Messenger; RNA, Ribosomal; Thermodynamics},
Month = {Jan},
Number = {1},
Pages = {133-48},
Pmc = {PMC326673},
Pmid = {6163133},
Pst = {ppublish},
Title = {Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information},
Volume = {9},
Year = {1981}}
Abstract = {Algorithmic prediction of RNA secondary structure has been an area of active inquiry since the 1970s. Despite many innovations since then, our best techniques are not yet perfect. The workhorses of the RNA secondary structure prediction engine are recursions first described by Zuker and Stiegler in 1981. These have well understood caveats; a notable flaw is the ad-hoc treatment of multi-loops, also called helical-junctions, that persists today. While several advanced models for multi-loops have been proposed, it seems to have been assumed that incorporating them into the recursions would lead to intractability, and so no algorithms for these models exist. Some of these models include the classical model based on Jacobson-Stockmayer polymer theory, and another by Aalberts and Nadagopal that incorporates two-length-scale polymer physics. We have realized practical, tractable algorithms for each of these models. However, after implementing these algorithms, we found that no advanced model was better than the original, ad-hoc model used for multi-loops. While this is unexpected, it supports the praxis of the current model.},
Author = {Ward, Max and Datta, Amitava and Wise, Michael and Mathews, David H},
Date-Added = {2017-08-13 23:07:22 +0000},
Date-Modified = {2017-08-13 23:07:22 +0000},
Doi = {10.1093/nar/gkx512},
Journal = {Nucleic Acids Res},
Journal-Full = {Nucleic acids research},
Month = {Jun},
Pmid = {28586479},
Pst = {aheadofprint},
Title = {Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best},
Year = {2017},
Bdsk-Url-1 = {}}
Abstract = {A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence.},
Author = {Ding, Y and Lawrence, C E},
Date-Added = {2017-08-13 22:29:33 +0000},
Date-Modified = {2017-08-13 22:29:33 +0000},
Journal = {Comput Chem},
Journal-Full = {Computers \& chemistry},
Mesh = {Algorithms; Animals; Base Sequence; Bayes Theorem; Escherichia coli; Molecular Sequence Data; Nucleic Acid Conformation; RNA, Ribosomal, 5S; RNA, Transfer, Ala; Xenopus laevis},
Month = {Jun},
Number = {3-4},
Pages = {387-400},
Pmid = {10404626},
Pst = {ppublish},
Title = {A bayesian statistical algorithm for RNA secondary structure prediction},
Volume = {23},
Year = {1999}}
Abstract = {The diversity and importance of the role played by RNAs in the regulation and development of the cell are now well-known and well-documented. This broad range of functions is achieved through specific structures that have been (presumably) optimized through evolution. State-of-the-art methods, such as McCaskill's algorithm, use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence. Although secondary structure predictions from thermodynamics-based algorithms are not as accurate as methods employing comparative genomics, the former methods are the only available tools to investigate novel RNAs, such as the many RNAs of unknown function recently reported by the ENCODE consortium. In this paper, we generalize the McCaskill partition function algorithm to sum over the grand canonical ensemble of all secondary structures of all mutants of the given sequence. Specifically, our new program, RNAmutants, simultaneously computes for each integer k the minimum free energy structure MFE(k) and the partition function Z(k) over all secondary structures of all k-point mutants, even allowing the user to specify certain positions required not to mutate and certain positions required to base-pair or remain unpaired. This technically important extension allows us to study the resilience of an RNA molecule to pointwise mutations. By computing the mutation profile of a sequence, a novel graphical representation of the mutational tendency of nucleotide positions, we analyze the deleterious nature of mutating specific nucleotide positions or groups of positions. We have successfully applied RNAmutants to investigate deleterious mutations (mutations that radically modify the secondary structure) in the Hepatitis C virus cis-acting replication element and to evaluate the evolutionary pressure applied on different regions of the HIV trans-activation response element. In particular, we show qualitative agreement between published Hepatitis C and HIV experimental mutagenesis studies and our analysis of deleterious mutations using RNAmutants. Our work also predicts other deleterious mutations, which could be verified experimentally. Finally, we provide evidence that the 3' UTR of the GB RNA virus C has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations. We hope that there will be long-term potential applications of RNAmutants in de novo RNA design and drug design against RNA viruses. This work also suggests potential applications for large-scale exploration of the RNA sequence-structure network. Binary distributions are available at},
Author = {Waldisp{\"u}hl, J{\'e}r{\^o}me and Devadas, Srinivas and Berger, Bonnie and Clote, Peter},
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