MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

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  • License: MIT License (MIT)
  • Author: Ammar Tareen and Justin B. Kinney
  • Tags genotype-phenotype maps, multiplex assays, variant effect, deep mutational scanning, massively parallel reporter assays
  • Requires: Python >=3.8
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MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

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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:

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