Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkageDeep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.
Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkageJ. N. K. Rao, Ruiqi Xin, Christian B. Macdonald et al.|bioRxiv (Cold Spring Harbor Laboratory)|2023 Abstract Deep mutational scanning (DMS) enables functional insight into protein mutations with multiplexed measurements of thousands of genetic variants in a protein simultaneously. The small sample size of DMS renders classical statistical methods ineffective, for example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace , a Bayesian framework for analyzing growth-based deep mutational scanning data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. To benchmark Rosace against existing methods, we developed Rosette , a simulation framework that simulates the distributional properties of DMS. Further, we show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools under Rosette simulation and real data.
Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-SYu Yan, Dean Wang, Ruiqi Xin et al.|STAR Protocols|2023 Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations. For complete details on the use and execution of this protocol, please refer to Yan et al (2023).1