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In Silico Prediction and Selection of Exon-Skipping Antisense Oligonucleotide Sequences Using eSkip-Finder
Shuntaro Chiba1, Yasushi Okuno2,3
1RIKEN Center for Computational Science, RIKEN, Yokohama, Japan.
Abstract:
In the early drug development using antisense oligonucleotides (ASOs) for exon-skipping treatment, researchers identify a region with high exon-skipping efficacy for a specific target exon, through experimental screening of ASOs. To streamline this process and reduce experimental effort, various in silico tools, including those for RNA structure modeling and prediction of skipping efficacy, have been developed. Among these tools, eSkip-Finder, a web-based server, has been developed to enhance the prediction capability of in silico skipping efficacy. It predicts exon-skipping efficacy based on user-input sequences and ASO length while providing a database with the capability for ASO search. This tool aids in designing effective ASOs for exon skipping and makes these resources accessible. Here, we introduce a method for selecting an ASO sequence using eSkip-Finder.
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