MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
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.
Additional Information
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Received 16 July 2021; Accepted 24 March 2022; Published 15 April 2022. This work was supported by NIH grant R35GM133777 (awarded to JBK), NIH Grant R35GM133613 (awarded to DMM), an Alfred P. Sloan Research Fellowship (awarded to DMM), a grant from the CSHL/Northwell Health partnership, and funding from the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. Review history: The review history is available as Additional file 2. Peer review information: Tim Sands was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Contributions: AT, WTI, DMM, and JBK conceived the project. AT and JBK wrote the software with assistance from AP and MK. WTI and JBK wrote a preliminary version of the software. AT, MK, and JBK performed the data analysis. JBK, AT, and DMM wrote the manuscript with contributions from MK and AP. All author(s) read and approved the final manuscript. Ethics approval and consent to participate: Not applicable. The authors declare that they have no competing interests.Attached Files
Published - 13059_2022_Article_2661.pdf
Submitted - 2020.07.14.201475v4.full.pdf
Supplemental Material - 13059_2022_2661_MOESM1_ESM.pdf
Supplemental Material - 13059_2022_2661_MOESM2_ESM.docx
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Additional details
- Alternative title
- MAVE-NN: Quantitative Modeling of Genotype-Phenotype Maps as Information Bottlenecks
- PMCID
- PMC9011994
- Eprint ID
- 104394
- Resolver ID
- CaltechAUTHORS:20200716-073040475
- R35GM133777
- NIH
- R35GM133613
- NIH
- Alfred P. Sloan Foundation
- Northwell Health
- Cold Spring Harbor Laboratory
- Simons Foundation
- Created
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2020-07-16Created from EPrint's datestamp field
- Updated
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2023-07-06Created from EPrint's last_modified field