HotKnots: Heuristic prediction of RNA secondary structures including pseudoknots

Ren, Jihong; Rastegari, Baharak; Condon, Anne; Hoos, Holger H.

doi:10.1261/rna.7284905

Cited by 249 publications

(226 citation statements)

References 38 publications

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“…HotKnots [115] adopts the strategy of pruning the initial helix space used to incrementally build the RNA structures. In this approach only sets of promising hotspots are used.…”

Section: Kinetic Foldingmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.

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“…HotKnots [115] adopts the strategy of pruning the initial helix space used to incrementally build the RNA structures. In this approach only sets of promising hotspots are used.…”

Section: Kinetic Foldingmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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“…Next, we analyzed the propensity of the 200, 120, 100, and 80 nt sequences surrounding the epigenetic clock CpGs to form pseudoknot and G4 structures. Hotknots [53,54] was applied to identify pseudoknot structures in the 100 nt region adjacent to clock CpGs, using Perl-based batch submissions to the Hotknots Web server. Folding predictions and their corresponding energy were also simulated using the local version of Kinefold [55].…”

Section: Methodsmentioning

confidence: 99%

Age-dependent methylation in epigenetic clock CpGs is associated with G-quadruplex, co-transcriptionally formed RNA structures and tentative splice sites

et al. 2018

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Horvath’s epigenetic clock consists of 353 CpGs whose methylation levels can accurately predict the age of individuals. Using bioinformatics analysis, we investigated the conformation, energy characteristics and presence of tentative splice sites of the sequences surrounding the epigenetic clock CpGs, in relation to the median methylation changes in different ages, the presence of CpG islands and their position in genes. Common characteristics in the 100 nt sequences surrounding the epigenetic clock CpGs are G-quadruplexes and/or tentative splice site motifs. Median methylation increases significantly in sequences which adopt less stable structures during transcription. Methylation is higher when CpGs overlap with G-quadruplexes than when they precede them. Median methylation in epigenetic clock CpGs is higher in sequences expressed as single products rather than in multiple products and those containing single donors and multiple acceptors. Age-related methylation variation is significant in sequences without G-quadruplexes, particularly those producing low stability nascent RNA and those with splice sites. CpGs in sequences close to transcription start sites and those which are possibly never expressed (hypothetical proteins) undergo similar extent of age-related median methylation decrease and increase. Preservation of methylation is observed in CpG islands without G-quadruplexes, contrary to CpGs far from CpG islands (open sea). Sequences containing G-quadruplexes and RNA pseudoknots, determining the recognition by H3K27 histone methyltransferase, are hypomethylated. The presented structural DNA and co-transcriptional RNA analysis of epigenetic clock sequences, foreshadows the association of age-related methylation changes with the principle biological processes of DNA and histone methylation, splicing and chromatin silencing.

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“…They restricted the analysis to the class of canonical simple recursive pseudoknots, achieving O(n 4 ) time, O(n 2 ) space, and leading to a program widely used 1 today. The program HotKnots [21] uses a heuristics to assemble pseudoknots from low-energy helices.…”

Section: Folding Pseudoknotsmentioning

confidence: 99%

Prediction of RNA Secondary Structure Including Kissing Hairpin Motifs

Theis

Janssen

Giegerich

2010

Lecture Notes in Computer Science

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Abstract. We present three heuristic strategies for folding RNA sequences into secondary structures including kissing hairpin motifs. The new idea is to construct a kissing hairpin motif from an overlay of two simple canonical pseudoknots. The difficulty is that the overlay does not satisfy Bellman's Principle of Optimality, and the kissing hairpin cannot simply be built from optimal pseudoknots. Our strategies have time/space complexities of O(n 4 )/O(n 2 ), O(n 4 )/O(n 3 ), and O(n 5 )/O(n 2 ). All strategies have been implemented in the program pKiss and were evaluated against known structures. Surprisingly, our simplest strategy performs best. As it has the same complexity as the previous algorithm for simple pseudoknots, the overlay idea opens a way to construct a variety of practically useful algorithms for pseudoknots of higher topological complexity within O(n 4 ) time and O(n 2 ) space.

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HotKnots: Heuristic prediction of RNA secondary structures including pseudoknots

Cited by 249 publications

References 38 publications

Computational methods in noncoding RNA research

Computational methods in noncoding RNA research

Age-dependent methylation in epigenetic clock CpGs is associated with G-quadruplex, co-transcriptionally formed RNA structures and tentative splice sites

Prediction of RNA Secondary Structure Including Kissing Hairpin Motifs

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