My preprint on keju, a statistical tool for Massively Parallel Reporter Assay (MPRA) data, is out! keju improves sensitivity, calibration, and reliability over previous methods by closely modeling important uncertainty sources in MPRAs. Check it out: www.biorxiv.org/content/10.6... (1/n)

Thanks Willow! It was great to work with you on this 😃

Happy to see this work out now in Genome Biology! Check out the final version here for your FACS DMS needs: link.springer.com/article/10.1...

Super excited to get this out. This collab started a few years ago and is the first paper from it. Here, with experimental and computational approaches we:

1. establish that cell villages can be just as accurate (one might argue more accurate!) than arrayed-based designs

bsky.app/profile/bior...

Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions Multi-phenotype deep mutational scanning (DMS) experiments provide a powerful means to dissect how protein variants affect different layers of molecular function, such as abundance, surface expression...

How do we decouple the effects of two functional phenotypes in protein deep mutational scanning (DMS)?
Meet Cosmos, our new statistical framework for causal inference in multi-phenotype DMS.
www.biorxiv.org/content/10.1...
[1/n]

figure 1 of Lilace paper detailing the data collection and analysis process to indicate where the Lilace model fits in

Statistical assessment of score uncertainty is also becoming especially critical as DMS experiments scale to more conditions, phenotypes, and proteins. We hope Lilace will help address these challenges and enable more reliable functional interpretation of FACS-based DMS data! (3/3)

Coupling DMS experiments with FACS enables simultaneous measurement of a quantitative phenotype (e.g. protein abundance) across thousands of mutations in a protein. However, modeling FACS as a phenotype can be challenging due to its unique multidimensional nature and experimental noise. (2/3)

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular ph...

Check out our new preprint on Lilace, a statistical tool for scoring FACS-based deep mutational scanning experiments! Lilace directly models the shift between variant fluorescence distributions and provides score uncertainty estimates to better assess reliability and reproducibility. (1/3)