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Status: Unchecked ×Topic: Genomics and Rare Diseases ×Clear all

4 claims from 2 papers

1. Biochemistry, Genetics and Molecular Biology › Genomics and Rare Diseases

   Accurate proteome-wide missense variant effect prediction with AlphaMissense

   Cheng, Novati, Pan et al. · Science · 2023

   AlphaMissense, an AlphaFold-based model fine-tuned on variant frequency data, predicts missense variant pathogenicity, and the authors release predictions for all possible human single amino acid substitutions.

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   1. Unchecked“By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data.”
   2. UncheckedA gene's average AlphaMissense pathogenicity score predicts whether it is essential to cells, including short essential genes that statistical methods struggle to detect.“The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect.”
   3. Unchecked“As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.”

2. Biochemistry, Genetics and Molecular Biology › Genomics and Rare Diseases

   Disease variant prediction with deep generative models of evolutionary data

   Frazer, Notin, Dias et al. · Nature · 2021

   The authors built EVE, a generative model of evolutionary sequence variation, to predict whether protein variants cause disease without using labels, and applied it to over 36 million variants.

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   1. UncheckedThe EVE model, trained without disease labels, is reported to beat label-trained prediction tools and match or beat high-throughput lab experiments.“Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification 12-16 .”