Flex-sweep: Singularity container and two pre-trained models

  • M. Elise Lauterbur (Contributor)
  • Kasper Munch (Contributor)
  • David Enard (Contributor)



Flex-sweep is a convolutional neural network (CNN) -based method of detecting selective sweeps. It is available at https://github.com/lauterbur/Flex-sweep, but is best run via a singularity container. This obviates the sometimes-tedious requirements of installing dependencies, except for the application to interface with the container itself, in this case singularity. The singularity container to run Flex-sweep is provided here. In addition, pre-trained models are provided for some common use-cases. The CNN training process can be time consuming, so these models make using Flex-sweep faster if they are appropriate for the data set to be classified.
Date made availableNov 14 2022

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