Evolutionary solution for the RNA design problem

Esmaili-Taheri, Ali; Ganjtabesh, Mohammad; Mohammad-Noori, M.

doi:10.1093/bioinformatics/btu001

Cited by 31 publications

(47 citation statements)

References 23 publications

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“…The genetic algorithms used to solve RNA design problem are MODENA [8], Frnakestein [9], GGI-FOLD [10] and ERD [1]. All of these algorithms apply four main steps of evolutionary algorithms but the way they implemented these steps is different.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…Last but not least, ERD is the only evolutionary algorithm and it uses mutation instead of recombination [1]. One advantage of ERD is that one can specify both energy and structural constraints.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…In this paper, we will introduce a new approach for RNA design problem that was based on the Simulated Annealing (SA) framework. Part of the algorithm was built upon an evolutionary algorithm ERD [1]. Our research showed that using SA as a meta-heuristic approach to find the global optimum on top of evolutionary algorithm has an outstanding performance in terms of Hamming distance between folded structure of designed sequence and target structure, and our preselection strategy can help to improve energy.…”

Section: Introductionmentioning

confidence: 99%

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SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies

Sav

Hampson

Tsang

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Most of the biological processes including expression levels of genes and translation of DNA to produce proteins within cells depend on RNA sequences, and the structure of the RNA plays vital role for its function. RNA design problem refers to the design of an RNA sequence that folds into given secondary structure. However, vast number of possible nucleotide combinations make this an NP-Hard problem. To solve the RNA design problem, a number of researchers have tried to implement algorithms using local stochastic search, context-free grammars, global sampling or evolutionary programming approaches. In this paper, we examine SIMARD, an RNA design algorithm that implements simulated annealing techniques. We also propose QPS, a mutation operator for SIMARD that pre-selects high quality sequences. Furthermore, we present experiment results of SIMARD compared to eight other RNA design algorithms using the Rfam datset. The experiment results indicate that SIMARD shows promising results in terms of Hamming distance between designed sequence and the target structure, and outperforms ERD in terms of free energy.978-1-5090-4240-1/16/$31.00 ©2016 IEEE

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies

Sav

Hampson

Tsang

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Finally, the sub-sequences are combined to design a sequence corresponding to the target structure. ERD (Esmaili-Taheri et al, 2014) uses the existing database of sequences (namely, STRAND [http://www.rnasoft.ca/strand/]) to design RNAs. In this algorithm, the target structure is divided into sub-structures such that each of them contains one multi-loop.…”

Section: Introductionmentioning

confidence: 99%

RNA design using simulated SHAPE data

2017

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It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research. The challenge of finding an RNA sequence for a given structure is known as the RNA design problem. Although there are several algorithms to solve this problem, they mainly consider hard constraints, such as minimum free energy, to evaluate the predicted sequences. Recently, SHAPE data has emerged as a new soft constraint for RNA secondary structure prediction. To take advantage of this new experimental constraint, we report here a new method for accurate design of RNA sequences based on their secondary structures using SHAPE data as pseudo-free energy. We then compare our algorithm with the four others: INFO-RNA, ERD, MODENA and RNAifold 2.0. Our algorithm precisely predicts 26 out of 29 new sequences for the structures extracted from the Rfam dataset, while the other four algorithms predict no more than 22 out of 29. The proposed algorithm is comparable to the above algorithms on RNA-SSD datasets, where they can predict up to 33 appropriate sequences for RNA secondary structures out of 34.

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“…. ), a wide array of computational methods [10,4,1,5,2,21,25,13,14,8,11,17,27,3,6] has been proposed to tackle the natural inverse version of the structure prediction, the RNA design problem. In this problem, one attempts to perform the in silico synthesis of artificial RNA sequences, performing a predefined biological function in vitro or in vivo.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial RNA Design: Designability and Structure-Approximating Algorithm in Watson–Crick and Nussinov–Jacobson Energy Models

et al. 2016

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We consider the Combinatorial RNA Design problem, a minimal instance of RNA design where one must produce an RNA sequence that adopts a given secondary structure as its minimal free-energy structure. We consider two free-energy models where the contributions of base pairs are additive and independent: the purely combinatorial Watson-Crick model, which only allows equallycontributing A − U and C − G base pairs, and the real-valued Nussinov-Jacobson model, which associates arbitrary energies to A − U, C − G and G − U base pairs.We first provide a complete characterization of designable structures using restricted alphabets and, in the four-letter alphabet, provide a complete characterization for designable structures without unpaired bases. When unpaired bases are allowed, we characterize extensive classes of (non-)designable structures, and prove the closure of the set of designable structures under the stutter operation. Membership of a given structure to any of the classes can be tested in Θ(n) time, including the generation of a solution sequence for positive instances.Finally, we consider a structure-approximating relaxation of the design, and provide a Θ(n) algorithm which, given a structure S that avoids two trivially non-designable motifs, transforms S into a designable structure constructively by adding at most one base-pair to each of its stems.

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Evolutionary solution for the RNA design problem

Cited by 31 publications

References 23 publications

SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies

SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies

RNA design using simulated SHAPE data

Combinatorial RNA Design: Designability and Structure-Approximating Algorithm in Watson–Crick and Nussinov–Jacobson Energy Models

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