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

Tareen, Ammar, Kooshkbaghi, Mahdi, Posfai, Anna, Ireland, William T, McCandlish, David M, Kinney, Justin B (April 2022) MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect. Genome Biology, 23 (1). p. 98. ISSN 1474-7596

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Abstract

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.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics Investigative techniques and equipment Investigative techniques and equipment > assays bioinformatics > computational biology bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > mutations
CSHL Authors:	Tareen, Ammar Kooshkbaghi, Mahdi Posfai, Anna McCandlish, David Kinney, Justin B.
Communities:	CSHL labs > Kinney lab CSHL labs > McCandlish lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	15 April 2022
Date Deposited:	20 Apr 2022 14:39
Last Modified:	17 Jan 2024 15:44
PMCID:	PMC9011994
URI:	https://repository.cshl.edu/id/eprint/40590

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