MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.
Genome Biol
; 23(1): 98, 2022 04 15.
Article
em En
| MEDLINE
| ID: mdl-35428271
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Bioensaio
/
Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article