Abstract
A variety of massively parallel assays for measuring high-resolution sequence-function relationships have been developed in recent years. However, software for learning quantitative models from these data is lacking. Here we describe Sort-Seq Tools, a software package that allows multiple types of quantitative models to be fit to massively parallel data in multiple different ways. We demonstrate Sort-Seq Tools on both simulated and published data from Sort-Seq studies, massively parallel reporter assays, and deep mutational scanning experiments. We observe that, as an inference method, information maximization generally outperforms both least squares optimization and enrichment ratio calculations.
Copyright
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