Abstract
We present the pathogenicity prediction models MetaRNN and MetaRNN-indel to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs) using deep learning and context annotations. Employing independent test datasets, we demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. MetaRNN executables and precomputed scores are available at http://www.liulab.science/MetaRNN.
Competing Interest Statement
The authors have declared no competing interest.
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