# Core B:  Data Management and Bioinformatics Core

> **NIH NIH U19** · HARVARD MEDICAL SCHOOL · 2020 · $363,451

## Abstract

Reconstructing the circuits that control how immune cells adopt specific states and control an immune 
response is a major challenge, due to the diversity of cell types, the spectra of cell states, and their temporal 
changes over the course of the response. The combined advent of massive scale single cell genomics and 
large scale genetic CRIPSR screens suddenly provide an extraordinary opportunity to reconstruct a systems 
level model of the complex molecular and cellular processes that unfold during an immune response, including 
the cell types and states that compose the response, the regulators that control them, how cells affect each 
other, and how they integrate to form physiological and pathological responses. However, to learn such 
knowledge from massive, noisy and heterogeneous data there is an enormous need for sophisticated, 
innovative, robust, and scalable computational methods. These span include early data quality control and 
processing that addresses noise such as false negatives in single cell RNA-seq profiles, and the inference of 
the regulators that control cell types, states and temporal transitions, and guidelines for adaptive experimental 
design, from the number of cells to analyze to the choice of genes to perturb. In particular, because the current 
capacity to perturb genes in vivo is limited, ranking candidates for perturbation and refining their predictions 
and ranking as new perturbation data is collected is key for successful discoveries. Finally, because of the 
complexity of the data and of the underlying biology, achieving insights requires a close partnership between 
immunologists and computational experts. Unfortunately, successful methods and foundational datasets often 
remain out of reach for immunologists, absent software and data portals that would serve those. 
Here, we will leverage our extensive and pioneering expertise in computational biology for systems 
immunology – which we developed, harnessed and demonstrated in a long-term and close collaboration with 
the members of this Program – to develop and deploy computational methods and tools to bridge the gap 
between data and knowledge in systems immunology, and apply them in the context of the program’s projects. 
Specifically, we will develop, establish and maintain cutting-edge tools for the analysis of single cell RNA-seq 
data including identification of cell types, states, temporal transitions, and the associated pathways and 
signature, with high efficiency compatible with massive scale data (Aim 1). We will develop, establish and 
maintain cutting-edge tools to predict key regulators associated with these cell types, states and responses, as 
they unfold over time, and develop and use methods that rank regulators and select targets for genetic 
manipulation in CRISPR screens in vivo, followed by adaptive identification of new regulators following 
additional genetic screens (Aim 2). We will establish and maintain a public portal for all ...

## Key facts

- **NIH application ID:** 9966862
- **Project number:** 5U19AI133524-04
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Orr Ashenberg
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $363,451
- **Award type:** 5
- **Project period:** 2017-07-05 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966862

## Citation

> US National Institutes of Health, RePORTER application 9966862, Core B:  Data Management and Bioinformatics Core (5U19AI133524-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9966862. Licensed CC0.

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