Jerome

Jerome

@jeromics.bsky.social

bioinformatics phd student at UCLA

138 followers486 following5 posts

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Jerome
@jeromics.bsky.social

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...

link.springer.comAccurate variant effect estimation in FACS-based deep mutational scanning data with Lilace - Genome BiologyDeep mutational scanning (DMS) coupled with fluorescence-activated cell sorting (FACS) provides a high-throughput method to link genetic variants with quantitative molecular phenotypes. Analysis of th...
Jerome@jeromics.bsky.social

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)

biorxiv.orgAccurate variant effect estimation in FACS-based deep mutational scanning data with LilaceDeep 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...

Jerome reposted

Matt Howard
@matthewkhoward.bsky.social

馃毃New preprint! Ligands at the same GPCR can produce wildly different signaling outputs. Why? We built a mutational scanning platform to measure ~10,000 碌OR variants across 15 opioids, yielding ~400k measurements of how sequence and ligand chemistry set signaling. www.biorxiv.org/content/10.6... 1/10

Jerome reposted

Harold Pimentel
@hjp.bsky.social

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...

bioRxiv Genetics@biorxiv-genetic.bsky.social

Cell villages and Dirichlet modeling map human cell fitness genetics https://www.biorxiv.org/content/10.1101/2025.09.26.678880v1

Jerome reposted

Jingyou Rao
@jingyour.bsky.social

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]

biorxiv.orgCosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein FunctionsMulti-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...
Jerome
@jeromics.bsky.social

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)

biorxiv.orgAccurate variant effect estimation in FACS-based deep mutational scanning data with LilaceDeep 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...
Jerome
@jeromics.bsky.social

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)

Jerome
@jeromics.bsky.social

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)

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