# Post-translational phenotypic profiling through nucleotide barcode sequencing > **NIH NIH R21** · UNIVERSITY OF CALIFORNIA BERKELEY · 2023 · $222,375 ## Abstract ABSTRACT Advances in DNA synthesis and sequencing now enable large-scale experiments that systematically explore how sequence variation affects molecular and cellular function. Transcriptional regulation has proven particularly amenable to massively parallel experiments that have revealed how sequence variation affects the activities of promoters and enhancers as well as transcription factors. These experiments have relied on high-throughput sequencing of the mRNAs produced during transcription as a direct read-out of regulatory activity. This elegant approach cannot be directly extended to study other important modes of biological regulation, however. Post-translational protein modification underlies nearly all cell signaling pathways. Despite its importance, we lack general approaches to assess how sequence variation affects post- translational regulation. We will address this gap with a broadly applicable strategy to link protein modifications, such as phosphorylation or regulated degradation, with a deep sequencing readout. Here we propose to develop this technique and validate it by demonstrating how protein-level regulation is affected by variation in the target protein, the modifying enzyme, and the broader genetic context of the cell. ## Key facts - **NIH application ID:** 10649344 - **Project number:** 1R21HG012991-01 - **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY - **Principal Investigator:** NICHOLAS T INGOLIA - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $222,375 - **Award type:** 1 - **Project period:** 2023-05-01 → 2025-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10649344 ## Citation > US National Institutes of Health, RePORTER application 10649344, Post-translational phenotypic profiling through nucleotide barcode sequencing (1R21HG012991-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10649344. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*