RNA secondary structure prediction with coincidence algorithm

Srikamdee, Supawadee; Wattanapornprom, Warin; Chongstitvatana, Prabhas

doi:10.1109/iscit.2016.7751721

Cited by 11 publications

(4 citation statements)

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“…In Figure 13 and Table 9, we present the comparison of StemP on 6 different 5s rRNA sequences with Genetic Algorithm (RNAPredict) [56], SA [15], two-level particle swarm optimization algorithm (TL-PSOfold) [28], RNAfold [31], Sarna-predict (SP) [54], Mfold [63], and Twolevel particle swarm optimization algorithm(COIN) [47]. The six sequences X67579(S.cerevisiae), AF03462(H.marismortui), X01590(T.aquaticus), AJ251080(G. stearothermophilus), V00336(E. coli), AE002087(D. radiodurans) are obtained from Gutell Lab [10] [56], SA [15], TL-PSOfold [28], RNAfold [31], SP [54], Mfold [63], and COIN [47]. result of TL-PSOfold is from [28], RNAPredict and Mfold from [56].…”

Section: Construct Edges Between Each Domain 4 a Maximal Clique That ...mentioning

confidence: 99%

“…FOLD, MaxExpect, ProbKnot all lost two pairs: U(80)-A(104) and A(81)-G(103) and MaxExpect found two extra pairs in (b). StemP is robust in predicting 5s rRNA sequences.In Figure13and Table9, we present the comparison of StemP on 6 different 5s rRNA sequences with Genetic Algorithm (RNAPredict)[56], SA[15], two-level particle swarm optimization algorithm (TL-PSOfold)[28], RNAfold[31], Sarna-predict (SP)[54], Mfold[63], and Twolevel particle swarm optimization algorithm(COIN)[47]. The six sequences X67579(S.cerevisiae), AF03462(H.marismortui), X01590(T.aquaticus), AJ251080(G. stearothermophilus), V00336(E. coli), AE002087(D. radiodurans) are obtained from Gutell Lab[10].…”

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confidence: 99%

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StemP: A fast and deterministic Stem-graph approach for RNA and protein folding prediction

Tang¹,

Kumbit²,

Kang³

2022

Preprint

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We propose a new deterministic methodology to predict RNA sequence and protein folding. Is stem enough for structure prediction? The main idea is to consider all possible stem formation in the given sequence. With the stem loop energy and the strength of stem, we explore how to deterministically utilize stem information for RNA sequence and protein folding structure prediction. We use graph notation, where all possible stems are represented as vertices, and co-existence as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for folding structure prediction. We introduce a Stem-Loop score to add structure information and to speed up the computation. The proposed method can handle secondary structure prediction as well as protein folding with pseudo knots. Numerical experiments are done using a laptop and results take only a few minutes or seconds. One of the strengths of this approach is in the simplicity and flexibility of the algorithm, and it gives deterministic answer. We explore protein sequences from Protein Data Bank, rRNA 5S sequences, and tRNA sequences from the Gutell Lab. Various experiments and comparisons are included to validate the propose method.

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Section: Construct Edges Between Each Domain 4 a Maximal Clique That ...mentioning

confidence: 99%

mentioning

confidence: 99%

StemP: A fast and deterministic Stem-graph approach for RNA and protein folding prediction

Tang¹,

Kumbit²,

Kang³

2022

Preprint

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“…Multiple sequences methods are more accurate but they require sets of RNA sequences and infer the predicted structure from the similarity of the input sequence with them. So, apart from our preliminary research [29], EDAs have not been applied to RNA secondary structure prediction problems with a single RNA sequence. A comparison of the Co-EDAs method to COIN [29] found that both algorithms have similar results.…”

Section: Introductionmentioning

confidence: 99%

“…So, apart from our preliminary research [29], EDAs have not been applied to RNA secondary structure prediction problems with a single RNA sequence. A comparison of the Co-EDAs method to COIN [29] found that both algorithms have similar results. However, the probability model of COIN is a matrix.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Learning of Estimation of Distribution Algorithm for RNA secondary structure prediction

Srikamdee¹,

Chongstitvatana²

2020

ECTI-CIT

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Estimation of distribution algorithms (EDAs) are successfully applied in the fields of bioinformatics for tasks such as gene structure analysis, protein structure prediction, and RNA secondary structure prediction. This paper proposes a new method, namely collaborative learning of estimation of distribution algorithms, or Co-EDAs, based on an estimation of distribution algorithm for RNA secondary structure prediction using a single RNA sequence as input. The proposed method consists of two EDAs with minimum free energy objective. The Co-EDAs use both good and poor solutions to improve the algorithm’s to search throughout the search space. Using information from poor solutions can indicate which area is unappealing to explore when searching with high-dimensional data. The Co-EDAs method was tested with 750 known RNA structures from RNA STRAND v2.0. That database includes data with more than 14 RNA types. The proposed method was compared to three prediction programs that are based on dynamic programming algorithms called Mfold, RNAfold, and RNAstructure. These programs are available as services on web servers. The results on average show that the Co-EDAs yields approximately 6% better accuracy than those competitors in all metrics.

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