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- Title
MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning.
- Authors
Li, Chang; Zhi, Degui; Wang, Kai; Liu, Xiaoming
- Abstract
Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN. The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN.
- Subjects
SINGLE nucleotide polymorphisms; DEEP learning; GENETIC variation; INDEPENDENT sets; PREDICTION models
- Publication
Genome Medicine, 2022, Vol 14, Issue 1, p1
- ISSN
1756-994X
- Publication type
Article
- DOI
10.1186/s13073-022-01120-z