Hong Zhenchen, Barton John P
Department of Physics and Astronomy, University of California, Riverside, USA.
Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, USA.
bioRxiv. 2024 Jan 31:2024.01.29.577759. doi: 10.1101/2024.01.29.577759.
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.
深度突变扫描（DMS）实验提供了一种强大的方法，可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可能难以分析，实验重复之间存在显著差异。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，用于从DMS数据中推断突变的功能效应。通过广泛测试，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。我们的方法灵活，可广泛应用于包含多个时间点、多个重复和不同实验条件的DMS数据。
