Evaluating the quality of SHAPE data simulated by k-mers for RNA structure prediction.
Identifieur interne : 000D95 ( Main/Exploration ); précédent : 000D94; suivant : 000D96Evaluating the quality of SHAPE data simulated by k-mers for RNA structure prediction.
Auteurs : Soheila Montaseri [Iran] ; Fatemeh Zare-Mirakabad [Iran] ; Mohammad Ganjtabesh [Iran]Source :
- Journal of bioinformatics and computational biology [ 1757-6334 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : RNA.
- methods : Computational Biology.
- Acylation, Databases, Factual, Models, Molecular, Nucleic Acid Conformation, Thermodynamics.
Abstract
Finding an effective measure to predict a more accurate RNA secondary structure is a challenging problem. In the last decade, an experimental method, known as selective [Formula: see text]-hydroxyl acylation analyzed by primer extension (SHAPE), was proposed to measure the tendency of forming a base pair for almost all nucleotides in an RNA sequence. These SHAPE reactivities are then utilized to improve the accuracy of RNA structure prediction. Due to a significant impact of SHAPE reactivity and in order to reduce the experimental costs, we propose a new model called HL-k-mer. This model simulates the SHAPE reactivity for each nucleotide in an RNA sequence. This is done by fetching the SHAPE reactivities for all sub-sequences of length k (k-mers) appearing in helix and loop regions. For evaluating the quality of simulated SHAPE data, ESD-Fold method is used based on the SHAPE data simulated by the HL-k-mer model ([Formula: see text]). Also, for further evaluation of simulated SHAPE data, three different methods are employed. We also extend this model to simulate the SHAPE data for the RNA pseudoknotted structure. The results indicate that the average accuracies of prediction using the SHAPE data simulated by our models (for [Formula: see text]) are higher compared to the experimental SHAPE data.
DOI: 10.1142/S0219720017500238
PubMed: 29113564
Affiliations:
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last="Zare-Mirakabad">Fatemeh Zare-Mirakabad</name><affiliation wicri:level="1"><nlm:affiliation>2 Department of Computer Science, Faculty of Mathematics and Computer Science, Amirkabir, University of Technology, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>2 Department of Computer Science, Faculty of Mathematics and Computer Science, Amirkabir, University of Technology, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Ganjtabesh, Mohammad" sort="Ganjtabesh, Mohammad" uniqKey="Ganjtabesh M" first="Mohammad" last="Ganjtabesh">Mohammad Ganjtabesh</name><affiliation wicri:level="1"><nlm:affiliation>1 Department of Computer Science, School of Mathematics Statistics, and Computer Science, University of Tehran, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>1 Department of Computer Science, School of Mathematics Statistics, and Computer Science, 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xml:lang="en"><term>RNA</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Acylation</term><term>Databases, Factual</term><term>Models, Molecular</term><term>Nucleic Acid Conformation</term><term>Thermodynamics</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>ARN</term><term>Acylation</term><term>Bases de données factuelles</term><term>Biologie informatique</term><term>Conformation d'acide nucléique</term><term>Modèles moléculaires</term><term>Thermodynamique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Finding an effective measure to predict a more accurate RNA secondary structure is a challenging problem. In the last decade, an experimental method, known as selective [Formula: see text]-hydroxyl acylation analyzed by primer extension (SHAPE), was proposed to measure the tendency of forming a base pair for almost all nucleotides in an RNA sequence. These SHAPE reactivities are then utilized to improve the accuracy of RNA structure prediction. Due to a significant impact of SHAPE reactivity and in order to reduce the experimental costs, we propose a new model called HL-k-mer. This model simulates the SHAPE reactivity for each nucleotide in an RNA sequence. This is done by fetching the SHAPE reactivities for all sub-sequences of length k (k-mers) appearing in helix and loop regions. For evaluating the quality of simulated SHAPE data, ESD-Fold method is used based on the SHAPE data simulated by the HL-k-mer model ([Formula: see text]). Also, for further evaluation of simulated SHAPE data, three different methods are employed. We also extend this model to simulate the SHAPE data for the RNA pseudoknotted structure. The results indicate that the average accuracies of prediction using the SHAPE data simulated by our models (for [Formula: see text]) are higher compared to the experimental SHAPE data.</div></front></TEI><affiliations><list><country><li>Iran</li></country></list><tree><country name="Iran"><noRegion><name sortKey="Montaseri, Soheila" sort="Montaseri, Soheila" uniqKey="Montaseri S" first="Soheila" last="Montaseri">Soheila Montaseri</name></noRegion><name sortKey="Ganjtabesh, Mohammad" sort="Ganjtabesh, Mohammad" uniqKey="Ganjtabesh M" first="Mohammad" last="Ganjtabesh">Mohammad Ganjtabesh</name><name sortKey="Zare Mirakabad, Fatemeh" sort="Zare Mirakabad, Fatemeh" uniqKey="Zare Mirakabad F" first="Fatemeh" last="Zare-Mirakabad">Fatemeh Zare-Mirakabad</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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