Metadata-Version: 2.4
Name: mavenn
Version: 1.1.4
Summary: MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Project-URL: Documentation, https://mavenn.readthedocs.io/en/v1.1.1/
Project-URL: Repository, https://github.com/jbkinney/mavenn
Author-email: "Ammar Tareen and Justin B. Kinney" <jkinney@cshl.edu>
License: The MIT License (MIT)
        
        Copyright (c) 2020 Ammar Tareen, Justin B. Kinney
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: deep mutational scanning,genotype-phenotype maps,massively parallel reporter assays,multiplex assays,variant effect
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8
Requires-Dist: logomaker
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pytest
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: tensorflow
Description-Content-Type: text/markdown

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
========================================================================
Compatible with Python 3.10.9+

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MAVE-NN enables the rapid quantitative modeling of genotype-phenotype (G-P) maps from the data produced by multiplex assays of variant effect (MAVEs). Such assays include deep mutational scanning (DMS) experiments on proteins, massively parallel reporter assays (MPRAs) on DNA or RNA regulatory sequences, and more. MAVE-NN conceptualizes G-P map inference as a problem in information compression; this problem is then solved by training a neural network using a TensorFlow backend. For installation instructions, tutorials, and documentation, please refer to the MAVE-NN website, https://mavenn.readthedocs.io/. For an extended discussion of this approach and its applications, please refer to our manuscript:

* Tareen, A., Kooshkbaghi, M., Posfai, A. et al. MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect. <em>Genome Biol</em> **23**, 98 (2022). https://doi.org/10.1186/s13059-022-02661-7

