Knotty: efficient and accurate prediction of complex RNA pseudoknot structures

Jabbari, Hosna; Wark, Ian William; Montemagno, Carlo; Will, Sebastian

doi:10.1093/bioinformatics/bty420

Cited by 46 publications

(43 citation statements)

References 28 publications

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“…We compared SPOT-RNA with 12 best available predictors. We downloaded the stand-alone version of mxfold 33 (available at https://github.com/keio-bioinformatics/mxfold), ContextFold 16 (available at https://www.cs.bgu.ac.il/negevcb/contextfold/), CONTRAfold 14 (available at http://contra.stanford.edu/contrafold/), Knotty 24 (available at https://github.com/HosnaJabbari/Knotty), IPknot 23 (available at http://rtips.dna.bio.keio.ac.jp/ipknot/), RNAfold 11 (available at https://www.tbi.univie.ac.at/RNA/), ProbKnot 22 (available at http://rna.urmc.rochester.edu/RNAstructure.html), CentroidFold 15 (available at https://github.com/satoken/centroid-rna-package), RNAstructure 12 (available at http://rna.urmc.rochester.edu/RNAstructure.html), RNAshapes 13 (available at https://bibiserv.cebitec.uni-bielefeld.de/rnashapes), pkiss 13 (available at https://bibiserv.cebitec.uni-bielefeld.de/pkiss), and CycleFold 27 (available at http://rna.urmc.rochester.edu/RNAstructure.html). In most of the cases, we used default parameters for secondary-structure prediction except for pkiss.…”

Section: Methodsmentioning

confidence: 99%

“…These base pairs include lone (unstacked), pseudoknotted (non-nested), and noncanonical (not A–U, G–C, and G–U) base pairs as well as triplet interactions 19,20 . While some methods can predict RNA secondary structures with pseudoknots (e.g., pknotsRG 21 , Probknot 22 , IPknot 23 , and Knotty 24 ) and others can predict noncanonical base pairs (e.g., MC-Fold 25 , MC-Fold-DP 26 , and CycleFold 27 ), none of them can provide a computational prediction for both, not to mention lone base pairs and base triplets.…”

Section: Introductionmentioning

confidence: 99%

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RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

et al. 2019

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The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only 250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of 10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

et al. 2019

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“…Thermodynamics-based algorithms [16,17,18] find the structure with the minimum free energy for an individual sequence using dynamic programming. Every substructure is assigned an empirically tested energy, and the energy of a structure is the sum of the energies for each substructure.…”

Section: Rna Secondary Structurementioning

confidence: 99%

KnotAli: Informed Energy Minimization Through the Use of Evolutionary Information

Gray¹

2021

Preprint

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BackgroundImproving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters. ResultsWe present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment and uses covariation and thermodynamic energy minimization to predict secondary structures for each individual sequence in the alignment. We compared KnotAli’s performance to that of three other alignment-based programs, on a large data set of 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT). We found KnotAli to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. ConclusionsWe find KnotAli’s predictions to be less dependent on alignment quality. In particular, KnotAli is shown to have more accurate predictions compared to other leading methods as alignment quality deteriorates. KnotAli can be found online on github at https://github.com/mateog4712/KnotAli

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“…Methods for MFE pseudoknot-free structure prediction use dynamic programming to find the MFE structure [15,16]. Since prediction of the MFE pseudoknotted structure is NPhard [17,18] and even inapproximable [19], methods for pseudoknotted MFE structure prediction focus on a restricted class of structures [20,21,22,23,24].…”

Section: A Secondary Structure Prediction For Interacting Moleculesmentioning

confidence: 99%

In silico prediction of COVID-19 test efficiency with DinoKnot

Newman

Chang

Jabbari

2021

2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus spreading across the world causing the disease COVID-19. COVID-19 is diagnosed by quantitative reverse-transcription polymer chain reaction (qRT-PCR) testing which utilizes different primer-probe sets depending on the assay used. Using in silico analysis we aimed to determine how the secondary structure of the SARS-CoV-2 RNA genome affects the interaction between the reverse primer during qRT-PCR and how it relates to the experimental primer-probe test efficiencies. We introduce the program DinoKnot (Duplex Interaction of Nucleic acids with pseudoKnots) that follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (DNA/RNA). DinoKnot is the first program that utilizes stable stems in both strands as a guide to predict their interaction structure. Using DinoKnot we predicted the interaction of the reverse primers used in four common COVID-19 qRT-PCR tests with the SARS-CoV-2 RNA genome. In addition, we predicted how 12 mutations in the primer/probe binding region may affect the primer/probe ability and subsequent SARS-CoV-2 detection. We identified three mutations that may prevent primer binding, reducing the ability for SARS-CoV-2 detection. Furthermore, we investigated the effect of mutations in two variants of concern (UK and South Africa) on the efficacy of the existing primer-probe sets. Despite mutations, we did not detect deviation in primer binding when compared to the reference target strand. We believe our contributions can aid in the design of more sensitive SARS-CoV-2 diagnosis tests.

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Knotty: efficient and accurate prediction of complex RNA pseudoknot structures

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 46 publications

References 28 publications

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

KnotAli: Informed Energy Minimization Through the Use of Evolutionary Information

In silico prediction of COVID-19 test efficiency with DinoKnot

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