دورية أكاديمية
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
| العنوان: | MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect |
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| المؤلفون: | Ammar Tareen, Mahdi Kooshkbaghi, Anna Posfai, William T. Ireland, David M. McCandlish, Justin B. Kinney |
| المصدر: | Genome Biology, Vol 23, Iss 1, Pp 1-27 (2022) |
| بيانات النشر: | BMC, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Biology (General) LCC:Genetics |
| مصطلحات موضوعية: | Biology (General), QH301-705.5, Genetics, QH426-470 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1474-760X |
| Relation: | https://doaj.org/toc/1474-760X |
| DOI: | 10.1186/s13059-022-02661-7 |
| URL الوصول: | https://doaj.org/article/d828facebaf94f6cbf3c37f56bf4cdb6 |
| رقم الأكسشن: | edsdoj.828facebaf94f6cbf3c37f56bf4cdb6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1474760X |
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| DOI: | 10.1186/s13059-022-02661-7 |