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Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace.

Jerome Freudenberg1, Jingyou Rao2, Matthew K Howard3,4

  • 1Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.

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Summary

Lilace is a new Bayesian model for analyzing deep mutational scanning experiments that use fluorescence-activated cell sorting. It accurately estimates variant effects and quantifies uncertainty, improving false discovery rates.

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Area of Science:

  • Genomics
  • Proteomics
  • Biostatistics

Background:

  • Deep mutational scanning (DMS) experiments assess genetic variant effects on protein function.
  • Fluorescence-activated cell sorting (FACS) is used to measure molecular phenotypes in DMS.
  • Analyzing FACS-based DMS data is challenging due to measurement variance and complex phenotypes.

Purpose of the Study:

  • To develop a statistical method for analyzing FACS-based DMS experiments.
  • To address the challenges of measurement variance and multidimensional phenotypes in FACS-DMS data.
  • To provide uncertainty quantification for variant effect estimates.

Main Methods:

  • Developed Lilace, a Bayesian statistical model.
  • Applied Lilace to simulated data for validation.
  • Tested Lilace on OCT1 and Kir2.1 FACS-based DMS experiments.

Main Results:

  • Lilace effectively estimates variant effects from FACS-based DMS data.
  • The model provides robust uncertainty quantification.
  • Demonstrated improved false discovery rate (FDR) while maintaining sensitivity.

Conclusions:

  • Lilace is a robust statistical tool for FACS-based DMS analysis.
  • The model enhances the accuracy and reliability of variant effect predictions.
  • Lilace offers a significant advancement for analyzing complex biological data from high-throughput experiments.