Abstract
Multiplex assays of variant effect (MAVEs) are being rapidly adopted in many areas of biology including gene regulation, protein science, and evolution. However, inferring quantitative models of genotype-phenotype maps from MAVE data remains a challenge. Here we introduce MAVE-NN, a neural-network-based Python package that addresses this problem by conceptualizing genotype-phenotype maps as information bottlenecks. We demonstrate the versatility, performance, and speed of MAVE-NN on a diverse range of published MAVE datasets. MAVE-NN is easy to install and is thoroughly documented at https://mavenn.readthedocs.io.
Competing Interest Statement
The authors have declared no competing interest.
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