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As the number and size of sequencing-based experiments grows, biologists and machine learning researchers can apply more complicated models to the prediction of the biological effects of variation in sequence. Interpreting these models is a challenge. I will discuss some of the methods we have applied to understanding sequence-based models, in particular, what we can learn with minimal understanding of the internals of the model itself.