In a novel study, researchers from the Icahn School of Medicine at Mount Sinai introduced LoGoFunc, an advanced computational tool that predicts pathogenic gain- and loss-of-function variants across the genome.
Unlike current methods that predominantly focus on loss of function, LoGoFunc distinguishes among different types of harmful mutations, offering potentially valuable insights into diverse disease outcomes. The findings were described in the November 30 online issue of Genome Medicine https://doi.org/10.1186/s13073-023-01261-9.
LoGoFunc uses machine learning trained on a database of known pathogenic gain-of-function and loss-of-function mutations identified in the literature. It considers a wide range of 474 biological features, including data from protein structures predicted by AlphaFold2 and network features reflecting human protein interactions. Tested on sets from the Human Gene Mutation Database and ClinVar, LoGoFunc demonstrated high accuracy in predicting gain-of-function, loss-of-function, and neutral variants, according to the investigators.
The investigators caution that while these findings are a significant step forward, translating them into clinical applications requires further validation and integration with other medical information. LoGoFunc's predictions are based on training data and inherent assumptions. Ongoing validation efforts are crucial for reliable outcomes. As genetic data continues to grow, refining LoGoFunc's capabilities and extending its scope are priorities for future research.
The tool's predictions for missense variants across the entire genome are accessible for non-commercial use and analysis at https://itanlab.shinyapps.io/goflof/.
