Multiobjective Metaheuristic to Design RNA Sequences

Rubio-Largo, Álvaro; Vanneschi, Leonardo; Castelli, Mauro; Vega‐Rodríguez, Miguel A.

doi:10.1109/tevc.2018.2844116

Cited by 17 publications

(13 citation statements)

References 47 publications

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“…We run each puzzle 10 times with different time limits and the results show that MoiRNAiFold solves most of them and many of them in seconds (Figure 2A ; resulting sequences in Supplementary Information, ‘EteRNA sequences’; section C). The best performing algorithm in the literature ( 24 ) solves 73 puzzles with a time limit of one day (although 61 of them are solved in less than a minute), while MoiRNAiFold solves 91 puzzles within the same time limit, 84 of them within a time limit of 1 minute (Figure 2A ) and, to the best of our knowledge, 12 of them for the first time (Figure 2B and Supplementary Figure SF4 ). A broad comparison of solved puzzles for several other algorithms (adapted from ( 24 , 34 )) is depicted in Supplementary Figure SF4 .…”

Section: Resultsmentioning

confidence: 99%

“…( 30 ); ( F ) In vitro cell-free assay comparison of GFP translation fold-change ratio (OFF/ ON) in 3WJ control from Kim et al. ( 24 ) compared to three MoiRNAiFold-generated 3WJ (Moirai BD 3WJ 1–3, orange , red and blue , respectively) at low and high sRNA. Results represent the AVG ± SEM of at least five experiments.…”

Section: Resultsmentioning

confidence: 99%

“…All of them are based on some form of heuristic local search to solve the RNA design problem (or RNA inverse folding problem). More recently, more sophisticated algorithms have been developed, some based on Reinforcement Learning ( 22 ), Deep Learning ( 23 ), MultiObjective Optimization ( 24 ) and others in collaborative approaches ( 25 ). The main goal of these approaches is to outperform each other in solving certain established benchmarks.…”

Section: Introductionmentioning

confidence: 99%

“…The main goal of these approaches is to outperform each other in solving certain established benchmarks. Even though NUPACK ( 19 ) seems to be the preferred method when attempting to design functional RNA sequences, it does not perform extremely well in benchmarks ( 24 ).…”

Section: Introductionmentioning

confidence: 99%

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MoiRNAiFold: a novel tool for complex in silico RNA design

Minuesa¹,

Alsina²,

García-Martín

et al. 2021

Nucleic Acids Research

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Novel tools for in silico design of RNA constructs such as riboregulators are required in order to reduce time and cost to production for the development of diagnostic and therapeutic advances. Here, we present MoiRNAiFold, a versatile and user-friendly tool for de novo synthetic RNA design. MoiRNAiFold is based on Constraint Programming and it includes novel variable types, heuristics and restart strategies for Large Neighborhood Search. Moreover, this software can handle dozens of design constraints and quality measures and improves features for RNA regulation control of gene expression, such as Translation Efficiency calculation. We demonstrate that MoiRNAiFold outperforms any previous software in benchmarking structural RNA puzzles from EteRNA. Importantly, with regard to biologically relevant RNA designs, we focus on RNA riboregulators, demonstrating that the designed RNA sequences are functional both in vitro and in vivo. Overall, we have generated a powerful tool for de novo complex RNA design that we make freely available as a web server (https://moiraibiodesign.com/design/).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MoiRNAiFold: a novel tool for complex in silico RNA design

Minuesa¹,

Alsina²,

García-Martín

et al. 2021

Nucleic Acids Research

View full text Add to dashboard Cite

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“…Machine Learning (ML) and optimisation are two different fundamental research fields in computer science [21,22]. Due to the rapid progress in the performance of computing and communication techniques, those two research areas have drawn widespread attention in a wide variety of applications and grown rapidly [66,145]. Although both fields belong to different communities, they are fundamentally based on artificial intelligence and the techniques from ML and optimisation interact frequently with each other as well as themselves in order to improve their learning and/or search capabilities.…”

Section: Introductionmentioning

confidence: 99%

A review on the self and dual interactions between machine learning and optimisation

2019

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Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from the latter search for the best option or solution to a given problem. To employ these techniques automatically and effectively aligning with the real aim of artificial intelligence, both sets of techniques are frequently hybridised, interacting with each other and themselves. This study focuses on such interactions aiming at (1) presenting a broad overview of the studies on self and dual interactions between machine learning and optimisation; (2) providing a useful tutorial for researchers and practitioners in both fields in support of collaborative work through investigation of the recent advances and analyses of the advantages and disadvantages of different techniques to tackle the same or similar problems; (3) clarifying the overlapping terminologies having different meanings used in both fields; (4) identifying research gaps and potential research directions.

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