ERD: a fast and reliable tool for RNA design including constraints

Esmaili-Taheri, Ali; Ganjtabesh, Mohammad

doi:10.1186/s12859-014-0444-5

Cited by 24 publications

(19 citation statements)

References 28 publications

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“…On the other hand, we prove that asymptotically, the expected time for a K-transition Monte Carlo trajectory is equal to that for a K-transition Gillespie trajectory multiplied by N . A rapidly emerging area of synthetic biology concerns the computational design of synthetic RNA [9,13] by using inverse folding software [48,57,19,15]. It seems clear that the next step in synthetic RNA design will be to control the kinetics of RNA folding.…”

Section: Relation Between Mfpt For Markov Chain Versus Processmentioning

confidence: 99%

RNA folding kinetics using Monte Carlo and Gillespie algorithms

Clote

Bayegan

2017

J. Math. Biol.

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RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary structure folding kinetics with respect to the Turner energy model for tiny (≈ 20 nt) RNA sequences, the folding kinetics for larger sequences can only be approximated by binning structures into macrostates in a coarse-grained model, or by repeatedly simulating secondary structure folding with either the Monte Carlo algorithm or the Gillespie algorithm.Here we investigate the relation between the Monte Carlo algorithm and the Gillespie algorithm. We prove that asymptotically, the expected time for a K-step trajectory of the Monte Carlo algorithm is equal to N times that of the Gillespie algorithm, where N denotes the Boltzmann expected network degree. If the network is regular (i.e. every node has the same degree), then the mean first passage time (MFPT) computed by the Monte Carlo algorithm is equal to MFPT computed by the Gillespie algorithm multiplied by N ; however, this is not true for non-regular networks. In particular, RNA secondary structure folding kinetics, as computed by the Monte Carlo algorithm, is not equal to the folding kinetics, as computed by the Gillespie algorithm, although the mean first passage times are roughly correlated.Simulation software for RNA secondary structure folding according to the Monte Carlo and Gillespie algorithms is publicly available, as is our software to compute the expected degree of the network of secondary structures of a given RNA sequence -see http://bioinformatics.bc.edu/clote/ RNAexpNumNbors.

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Section: Relation Between Mfpt For Markov Chain Versus Processmentioning

confidence: 99%

RNA folding kinetics using Monte Carlo and Gillespie algorithms

Clote

Bayegan

2017

J. Math. Biol.

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“…ERD is an evolutionary algorithm the emphasizes mutation over recombination [9] [10]. ERD was designed to take into consideration energy and structural constraints in order to provide structures that are closer to natural structures both in terms of structural distance and free energy [9].…”

Section: Rna Design -Evolutionary Algorithmsmentioning

confidence: 99%

Examining the annealing schedules for RNA design algorithm

Erhan

Sav

Kalashnikov

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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RNA structures are important for many biological processes in the cell. One important function of RNA are as catalytic elements. Ribozymes are RNA sequences that fold to form active structures that catalyze important chemical reactions. The folded structure for these RNA are very important; only specific conformations maintain these active structures, so it is very important for RNA to fold in a specific way. The RNA design problem describes the prediction of an RNA sequence that will fold into a given RNA structure. Solving this problem allows researchers to design RNA; they can decide on what folded secondary structure is required to accomplish a task, and the algorithm will give them a primary sequence to assemble. However, there are far too many possible primary sequence combinations to test sequentially to see if they would fold into the structure. Therefore we must employ heuristics algorithms to attempt to solve this problem. This paper introduces SIMARD, an evolutionary algorithm that uses an optimization technique called simulated annealing to solve the RNA design problem. We analyzes three different cooling schedules for the annealing process: 1) An adaptive cooling schedule, 2) a geometric cooling schedule, and 3) a geometric cooling schedule with warm up. Our results show that an adaptive annealing schedule may not be more effective at minimizing the Hamming distance between the target structure and our folded sequence's structure when compared with geometric schedules. The results also show that warming up in a geometric cooling schedule may be useful for optimizing SIMARD.

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“…To our knowledge, the only other web servers or software for RNA design are: ( 4 ), ( 3 ), ( 19 ), ( 6 ), ( 2 ), ( 1 ), (web server is called RNAdesigner) ( 20 ), ( 5 ), ( 14 ). Default parameters were used for all software, with the exception of , where we used flags .…”

Section: Comparison With Other Softwarementioning

confidence: 99%

RNAiFold 2.0: a web server and software to design custom and Rfam-based RNA molecules

2015

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Several algorithms for RNA inverse folding have been used to design synthetic riboswitches, ribozymes and thermoswitches, whose activity has been experimentally validated. The software is unique among approaches for inverse folding in that (exhaustive) constraint programming is used instead of heuristic methods. For that reason, can generate all sequences that fold into the target structure or determine that there is no solution. is a complete overhaul of , rewritten from the now defunct COMET language to C++. The new code properly extends the capabilities of its predecessor by providing a user-friendly pipeline to design synthetic constructs having the functionality of given Rfam families. In addition, the new software supports amino acid constraints, even for proteins translated in different reading frames from overlapping coding sequences; moreover, structure compatibility/incompatibility constraints have been expanded. With these features, allows the user to design single RNA molecules as well as hybridization complexes of two RNA molecules. Availability: the web server, source code and linux binaries are publicly accessible at http://bioinformatics.bc.edu/clotelab/RNAiFold2.0.

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ERD: a fast and reliable tool for RNA design including constraints

Cited by 24 publications

References 28 publications

RNA folding kinetics using Monte Carlo and Gillespie algorithms

RNA folding kinetics using Monte Carlo and Gillespie algorithms

Examining the annealing schedules for RNA design algorithm

RNAiFold 2.0: a web server and software to design custom and Rfam-based RNA molecules

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