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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URL: https://www.ncbi.nlm.nih.gov/pubmed/35428271
DOI: 10.1186/s13059-022-02661-7

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 > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification
bioinformatics > computational biology
CSHL Authors:
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: 22 Apr 2022 13:49
PMCID: PMC9011994
URI: https://repository.cshl.edu/id/eprint/40590

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