Indel

Phenotype

Biology

Gene

Genotype

Machine learning

Nonsynonymous substitution

Genome

Paleontology

Single-nucleotide polymorphism

Artificial intelligence

Context (archaeology)

Computer science

Computational biology

Missense mutation

Frameshift mutation

Genetics

Genome Medicine

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 .

MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning