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Prediction tool for pathogenicity of start-lost variants.

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PoStaL

PoStaL is a machine learning-based prediction tool for Pathogenicity of Start-Lost variants. This page provides;

  1. Full list of pathogenicity scores of any possible start-lost variants in canonical transcripts defined by SnpEff (postal_all.txt)
  2. Features used for model construction (postal_features.txt)
  3. R objects of the constructed model (postal_model.obj)
  4. R source code to reproduce the figures in our publication (postal_fig.txt)
  5. Full list of the length of amino acid residues extended by a stop-lost variant (stopAA.txt)

Email: atsushi.takata@riken.jp or atakata@yokohama-cu.ac.jp

Citation: Refinement of the clinical variant interpretation framework by statistical evidence and machine learning
Atsushi Takata, Kohei Hamanaka and Naomichi Matsumoto
Med 2021 DOI: https://doi.org/10.1016/j.medj.2021.02.003

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Prediction tool for pathogenicity of start-lost variants.

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