eSkip-Finder

an online exon-skipping antisense oligonucleotide resource

Welcome!


eSkip-Finder is an online resource for helping researchers identify optimal sequences of antisense oligonucleotides (ASOs) for exon skipping.

  Search the Database

Last updated on Nov 25, 2021
    Gene name/Gene symbol
    Species
    Exon
    ASO chemistry
    ASO sequence
        

  Predict the efficacy of exon-skipping ASOs

Last updated on Dec 20, 2020
    Species
    Length
    ASO chemistry
    200 bp upstream
     intron sequence
 
    Exon sequence
    
    200 bp downstream
     intron sequence
 
      

Prediction may take a few minutes.

Welcome to eSkip-Finder!


eSkip-Finder is the first machine learning-based design tool and database of antisense oligonucleotides (ASOs) for exon skipping. During the past 10 years, antisense-mediated exon skipping has proven to be a powerful tool for correction of mRNA splicing. For example, recently FDA-approved antisense oligonucleotides, including viltolarsen, eteplirsen, golodirsen, and milasen, were developed based on exon skipping technology. A significant challenge, however, is the difficulty in selecting an optimal target sequence for exon skipping. We have developed a computational method that takes into account many parameters as well as experimental data to design highly effective ASOs for exon skipping1, and improved this frame using a machine-learning algorithm. eSkip-Finder was developed by an international team of experts in computer science, neurology, and genetics, including Drs. Shuntaro Chiba and Yasushi Okuno at the Molecular Design Data Intelligence Unit, RIKEN, Dr. Yoshitsugu Aoki at the Department of Molecular Therapy, National Center of Neurology and Psychiatry, and Dr.Toshifumi Yokota at the Department of Medical Genetics, University of Alberta, Faculty of Medicine and Dentistry.

1  Echigoya Y, Mouly V, Garcia L, Yokota T, Duddy W, In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy. PLoS ONE, 2015, 10(3): e0120058.
2  Chiba et al., eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping, Nucleic Acids Research, 2021, gkab442, https://doi.org/10.1093/nar/gkab442

Citation


When using eSkip-finder, please cite the paper below:

Chiba et al., eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping, Nucleic Acids Research, 2021, gkab442, https://doi.org/10.1093/nar/gkab442

Disclaimer


The information and services provided on this website are for general information purposes only. While we endeavor to keep the information up to date and correct, we make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability with respect to the website or the information, products, services, or related graphics contained on the website for any purpose. Any reliance you place on such information is therefore strictly at your own risk.
In no event will we be liable for any loss or damage including without limitation, indirector consequential loss or damage, or any loss or damage whatsoever arising from loss of data or profits arising out of, or in connection with, the use of this website.
Through this website, you can link to other websites and third-party content which are not under the control of the eSkip-Finder Team. We have no control over the nature, content, and availability of those sites. The inclusion of any links does not necessarily imply a recommendation or endorse the views expressed within them.
Every effort is made to keep the website up and running smoothly. However, the eSkip-Finder Team takes no responsibility for, and will not be liable for, the website being unavailable for any reason, including but not limited to technical issues beyond our control.

Caveats


eSkip-Finder does not take into account whether predicted ASOs can be technically synthesized. There is a possibility that highly ranked ASOs cannot be synthesized due to high GC contents etc.

Contact us


Please address your comments, questions, and suggestions to:


  Shuntaro Chiba   
  Yoshitsugu Aoki   
  Toshifumi Yokota