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MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning

Chang Li, Degui Zhi, Kai Wang, View ORCID ProfileXiaoming Liu
doi: https://doi.org/10.1101/2021.04.09.438706
Chang Li
1USF Genomics & College of Public Health, University of South Florida, Tampa, FL, USA
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Degui Zhi
2School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Kai Wang
3Children’s Hospital of Philadelphia & Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Xiaoming Liu
1USF Genomics & College of Public Health, University of South Florida, Tampa, FL, USA
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  • ORCID record for Xiaoming Liu
  • For correspondence: xiaomingliu@usf.edu
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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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 11, 2021.
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MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning
Chang Li, Degui Zhi, Kai Wang, Xiaoming Liu
bioRxiv 2021.04.09.438706; doi: https://doi.org/10.1101/2021.04.09.438706
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MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning
Chang Li, Degui Zhi, Kai Wang, Xiaoming Liu
bioRxiv 2021.04.09.438706; doi: https://doi.org/10.1101/2021.04.09.438706

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