ArticleGenome biology2024
Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Cardiomyocyte vulnerability to lamin polymer disruption revealed by saturation mutagenesis.bioRxiv : the preprint server for biology · 2026Article
- Combining multiplexed functional data to improve variant classification.Genome medicine · 2026Article
- Using Domain Insertion to Create Sulfite Reductases That Present Chemical-Dependent Activities.ACS synthetic biology · 2026Article
- CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis.bioRxiv : the preprint server for biology · 2026Article
- keju: powerful and accurate inference in Massively Parallel Reporter Assays.bioRxiv : the preprint server for biology · 2026Article
- Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace.Genome biology · 2026Article
- High-throughput mapping of 6,888bioRxiv : the preprint server for biology · 2026Article
- Article
- Rosace-AA: enhancing interpretation of deep mutational scanning data with amino acid substitution and position-specific insights.Bioinformatics advances · 2026Article
- Comprehensive mutational characterization of the calcium-sensing STIM1 EF-hand reveals residues essential for structure and function.Genetics · 2025Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Saturation mapping of MUTYH variant effects using DNA repair reporters.American journal of human genetics · 2025Article
- Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions.bioRxiv : the preprint server for biology · 2025Article
- Article
- Article
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning.bioRxiv : the preprint server for biology · 2024Article
- MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.Genome biology · 2024Article
- Molecular basis of proton-sensing by G protein-coupled receptors.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
Deep 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.
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