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

García-Martín, Juan Antonio; Dotú, Iván; Clote, Peter

doi:10.1093/nar/gkv460

Cited by 30 publications

(37 citation statements)

References 33 publications

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“…In this study, HRDSSD algorithm is run on a machine with Intel(R) Core(TM) i5-2450M CPU 2.50 GHz and 4 GB RAM. We constrained the running time of the proposed algorithm to 10 minutes (Garcia-Martin et al, 2015) in each execution. We compared three different versions of the proposed algorithm, Our model (HRDSSD), Sükösd model (HRDSSD-suko) and one without using SHAPE data (HRDWSD), to show that the simulated SHAPE data can improve the algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…We compared three different versions of the proposed algorithm, Our model (HRDSSD), Sükösd model (HRDSSD-suko) and one without using SHAPE data (HRDWSD), to show that the simulated SHAPE data can improve the algorithm. Then, HRDSSD is compared to some well-known algorithms, such as ERD (EsmailiTaheri et al, 2014), RNAifold 2.0 (Garcia-Martin et al, 2015), MODENA (Taneda, 2011) and INFO-RNA (Busch and Backofen, 2006). …”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

RNA design using simulated SHAPE data

2017

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“…Unlike models A01-A04, models A05-A37 were built from the set of benchmarking single stranded RNAs given by Garcia et al [14,28]. These single stranded RNAs are regulatory RNAs that are able to function in Nature.…”

Section: Performance Assessmentmentioning

confidence: 99%

“…"Multi-state structures" refers to two RNA changeable molecules that can fold into three distinct structures; i.e., two independent structures of two RNA molecules that further interact to form a third independent hybridized RNA molecule structure. Nevertheless, only a few RNA tools such as NUPACK [13], RNAiFold [14,28], RiboMaker [29] and iDoDe [30] are able to cope with this challenge. NUPACK is a remarkable design tool that provides nucleotide sequences for supporting the inverse folding problem.…”

Section: Introductionmentioning

confidence: 99%

“…The design method attempts to find optimal sequences of two RNA molecules whose MFE hybridized structure is identical to a target hybridized structure. However, RNAiFold does not support multi-state structures [14,28]. Recently, RiboMaker has been developed for providing RNA sequences of two molecules corresponding to multi-state target structures [26,29].…”

Section: Introductionmentioning

confidence: 99%

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iDoRNA: An Interacting Domain-based Tool for Designing RNA-RNA Interaction Systems

Thaiprasit

Kaewkamnerdpong

Waraho-Zhmayev

et al. 2016

Entropy

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RNA-RNA interactions play a crucial role in gene regulation in living organisms. They have gained increasing interest in the field of synthetic biology because of their potential applications in medicine and biotechnology. However, few novel regulators based on RNA-RNA interactions with desired structures and functions have been developed due to the challenges of developing design tools. Recently, we proposed a novel tool, called iDoDe, for designing RNA-RNA interacting sequences by first decomposing RNA structures into interacting domains and then designing each domain using a stochastic algorithm. However, iDoDe did not provide an optimal solution because it still lacks a mechanism to optimize the design. In this work, we have further developed the tool by incorporating a genetic algorithm (GA) to find an RNA solution with maximized structural similarity and minimized hybridized RNA energy, and renamed the tool iDoRNA. A set of suitable parameters for the genetic algorithm were determined and found to be a weighting factor of 0.7, a crossover rate of 0.9, a mutation rate of 0.1, and the number of individuals per population set to 8. We demonstrated the performance of iDoRNA in comparison with iDoDe by using six RNA-RNA interaction models. It was found that iDoRNA could efficiently generate all models of interacting RNAs with far more accuracy and required far less computational time than iDoDe. Moreover, we compared the design performance of our tool against existing design tools using forty-four RNA-RNA interaction models. The results showed that the performance of iDoRNA is better than RiboMaker when considering the ensemble defect, the fitness score and computation time usage. However, it appears that iDoRNA is outperformed by NUPACK and RNAiFold 2.0 when considering the ensemble defect. Nevertheless, iDoRNA can still be an useful alternative tool for designing novel RNA-RNA interactions in synthetic biology research. The source code of iDoRNA can be downloaded from the site http://synbio.sbi.kmutt.ac.th.

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