MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.

Automated summary

Multiplex assays of variant effect (MAVEs) are a family of methods used to study the effects of mutations on proteins and gene regulatory sequences. Researchers have developed a neural-network-based Python package called MAVE-NN, which can learn genotype-phenotype maps from MAVE datasets, including biophysically interpretable models. It can effectively deconvolve mutational effects from experimental nonlinearities and noise.