Ontology type: schema:ScholarlyArticle Open Access: True
2022-04-15
AUTHORSAmmar Tareen, Mahdi Kooshkbaghi, Anna Posfai, William T. Ireland, David M. McCandlish, Justin B. Kinney
ABSTRACTMultiplex 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. More... »
PAGES98
http://scigraph.springernature.com/pub.10.1186/s13059-022-02661-7
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