Bi-objective integer programming for RNA secondary structure prediction with pseudoknots

Background RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution,...

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Veröffentlicht in:BMC bioinformatics Jg. 19; H. 1; S. 13 - 15
Hauptverfasser: Legendre, Audrey, Angel, Eric, Tahi, Fariza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 15.01.2018
BioMed Central Ltd
BMC
Schlagworte:
Algorithms
Bi-objective
Bioinformatics
Biomedical and Life Sciences
Computational biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Science
Integer programming
Life Sciences
Microarrays
Optimal solutions
Pseudoknot
Research Article
RNA
Secondary structure
Structural analysis
Integer programming
Pseudoknot
Optimal solutions
RNA
Sub-optimal solutions
Bi-objective
Secondary structure
RNA Secondary structure
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. Results We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. Conclusion The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
PMCID: PMC5769536
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2007-7