Abstract
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning (DMS) experiments on proteins and massively parallel reporter assays (MPRAs) on gene regulatory sequences. However, a general strategy for inferring quantitative models of genotype-phenotype (G-P) 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 G-P 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Moderate revisions throughout.