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Paper   IPM / Biological Sciences / 15158
School of Biological Sciences
  Title:   Evaluating the quality of SHAPE data simulated by k-mers for RNA structure prediction
1.  Soheila Montaseri
2.  Fatemeh Zare-Mirakabad
3.  Mohammad Ganjtabesh
  Status:   Published
  Journal: world scientific
  No.:  6
  Vol.:  15
  Year:  2017
  Pages:   10.1142/S0219720017500238
  Supported by:  IPM
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 2′-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 (k=2, 3, 4). 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 k=2, 3) are higher compared to the experimental SHAPE data.

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