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bioRxiv · 10.1101/2020.07.14.201475

MAVE-NN: Quantitative Modeling of Genotype-Phenotype Maps as Information Bottlenecks

Tareen, A.·Ireland, W. T.·Posfai, A.·McCandlish, D. M.·Kinney, J. B.
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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.

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Tareen, A., Ireland, W. T., Posfai, A., McCandlish, D. M., Kinney, J. B.. 2020-07-14. MAVE-NN: Quantitative Modeling of Genotype-Phenotype Maps as Information Bottlenecks. https://doi.org/10.1101/2020.07.14.201475

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