# Multi-omic phenotyping of human transcriptional regulators

> **NIH NIH U01** · JACKSON LABORATORY · 2024 · $553,390

## Abstract

PROJECT SUMMARY
The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) program is using multiple perturbation strategies to
realize the NHGRI's vision of assigning function to every human gene. Strategies include pooled and individual
gene knockouts and knockdowns (KDs), generated using CRISPR technologies and auxin-inducible degrons.
Following application of such assays, molecular phenotypes of the cells are profiled longitudinally and at
individual time points using bulk and single-cell (sc)RNA-seq. Perturbation strategies have intrinsic sources of
variability, e.g., KD penetrance, while the single-cell sequencing approaches contribute technical noise, e.g.,
`drop out.' Quantifying and controlling this variability are crucial to ensure reliable phenotypic assessment and
fulfill MorPhiC's goal to accurately catalog gene function. Given the critical role of transcription factors (TFs) in
regulating cell state, all four MorPhiC Data Production Centers (DPCs) will perturb TFs and then profile cells
using bulk or sc-RNA-seq. A wide range of other `regulatory phenotyping' data, including (bulk or single-cell)
ATAC-seq, are being generated within MorPhiC and TF ChIP-seq, HiC, and massively parallel reporter assay
(MPRA) data are available in the ENCODE and Impact of Genomic Variation on Function (IGVF) consortia. To
robustly define the regulatory impact of TF perturbation, we propose a JAX MorPhiC Data Analysis and Validation
Center (DAV) to analyze these multi-modal data. Our team is uniquely positioned to establish this TF-focused
DAV: we are co-located with the JAX MorPhiC DPC and have consortium-level collaborations with its PI, while
our own work focuses on elucidating transcriptional regulation of genes and on developing robust computational
methods through community efforts. In Aim 1, we will quantify and control variability in perturbation-based
regulatory phenotyping by using heterogeneous data generated within MorPhiC to isolate their technical noise
characteristics and to derive a set of TF-gene target pairs (TF-GTs). We will then computationally simulate large-
scale perturbation screens, through which we will perform power analysis to quantify data variability and make
recommendations that ameliorate it. In Aim 2, we will evaluate published gene regulatory network (GRN)
inference methods. We will also conduct two “crowd-sourced” DREAM Challenges, in which community
participants will develop GRN inference methods that we will objectively evaluate with MorPhiC data. Using top-
performing methods, as well as a novel approach we are developing based on dynamical systems, we will
perform in silico TF perturbation within the GRNs to prioritize TFs for experimental validation in MorPhiC. In Aim
3, we will further improve robustness of inferred TF-GTs by integrating them with TF ChIP-seq, HiC, and MPRA
data, knockout mouse phenotyping data (KOMP2), and spatial transcriptomics data from JAX and MorPhiC. We
will validate published methods for ...

## Key facts

- **NIH application ID:** 10927399
- **Project number:** 5U01HG013175-02
- **Recipient organization:** JACKSON LABORATORY
- **Principal Investigator:** Brian S White
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $553,390
- **Award type:** 5
- **Project period:** 2023-09-11 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10927399

## Citation

> US National Institutes of Health, RePORTER application 10927399, Multi-omic phenotyping of human transcriptional regulators (5U01HG013175-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10927399. Licensed CC0.

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