Department of Physics and Astronomy, University of California, Riverside, CA 92521, United States.
Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, PA 15260, United States.
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae499.
Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.
popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.
深度突变扫描（DMS）实验提供了一种强大的方法，可以大规模测量基因突变的功能效应。然而，这些实验产生的数据很难分析，实验重复之间存在很大差异。为了克服这一挑战，我们开发了 popDMS，这是一种基于群体遗传学理论的计算方法，可从 DMS 数据中推断突变的功能效应。通过广泛的测试，我们发现 popDMS 推断的单突变和上位性的功能效应在重复实验中高度一致，与现有方法相比具有优势。我们的方法具有灵活性，可以广泛应用于包括多个时间点、多个重复和不同实验条件的 DMS 数据。
popDMS 是用 Python 和 Julia 实现的，并在 GitHub 上免费提供，网址为 https://github.com/bartonlab/popDMS。
