An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots

Ruan, Jianhua; Stormo, Gary D.; Zhang, Weixiong

doi:10.1093/bioinformatics/btg373

Cited by 201 publications

(175 citation statements)

References 35 publications

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“…We also compared the prediction results based on the datasets used in the work of Ruan et al [41]. Again, the percentage of correctly predicted base pairs of the filled hxmatch prediction is the same or even higher as in the ilm predictions.…”

Section: Discussionmentioning

confidence: 94%

“…The hxmatch program in combination with RNAalifold needs about seconds for the structure prediction of a 16SrRNA on a Linux PC with a Dual XEON P4 2.2 Ghz. For comparison ilm [41] takes about 5min for the same task.…”

Section: Post-processing the Maximum Weighted Matching Obtained For mentioning

confidence: 99%

“…A related approach by Ruan et al [41] uses the same weight matrix as Tabaska's program but replaces the solution of the MWM Problem by an iterated loop matching algorithm. One first solves the Maximum Circular Matching [36] to obtain a pseudoknot-free secondary structure and then repeats the computation on the remaining un-paired bases in order to insert pseudoknots, iterating the procedure until no further base pairs can be found.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of consensus RNA secondary structures including pseudoknots

Witwer

Hofacker²,

Stadler³

2004

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Most functional RNA molecules have characteristic structures that are highly conserved in evolution. Many of them contain pseudoknots. Here we present a method for computing the consensus structures including pseudoknots based on alignments of a few sequences. The algorithm combines thermodynamic and covariation information to assign scores to all possible base pairs, the base pairs are chosen with the help of the maximum weighted matching algorithm. We applied our algorithm to five different types of RNA known to contain pseudoknots. All pseudoknots were predicted correctly, and more than 85% of the base pairs were identified.

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Section: Discussionmentioning

confidence: 94%

Section: Post-processing the Maximum Weighted Matching Obtained For mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of consensus RNA secondary structures including pseudoknots

Witwer

Hofacker²,

Stadler³

2004

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…If a score system such as mutual information is used to describe covariations, methods such as Nussinov's algorithm [104], Maximum Weighted Matching [136] or Iterated Loop Matching [121,122]-all used to predict the secondary structure of a single molecule (Sect. 4.1)-can be applied to detect the optimal consensus secondary structure [41].…”

Section: Covariation Only Methodsmentioning

confidence: 99%

“…The same simple scoring scheme can be used with the ideas of Kinetic Analysis and Fold Space Analysis, out of the context of thermodynamics. 7 Iterated Loop Matching (ILM) [121,122] performs the original Nussinov's algorithm iteratively. In each iteration the best helix is selected for the final structure and cut out of the sequence for the next iteration.…”

Section: Other Approachesmentioning

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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