PoStaL is a machine learning-based prediction tool for Pathogenicity of Start-Lost variants. This page provides;
- Full list of pathogenicity scores of any possible start-lost variants in canonical transcripts defined by SnpEff (postal_all.txt)
- Features used for model construction (postal_features.txt)
- R objects of the constructed model (postal_model.obj)
- R source code to reproduce the figures in our publication (postal_fig.txt)
- 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