# Data Science Tools to Increase Insight in Genomics Data

> **NIH NIH R35** · JOHNS HOPKINS UNIVERSITY · 2024 · $462,324

## Abstract

Project summary
High-throughput molecular technologies are increasingly being used in biomedical and basic science. New tech-
nologies and assays are being developed at a rapid pace, and have the potential to interrogate cellular processes
at an unprecedented resolution and throughput. As a result of the developments in measurement technologies,
more investigators in molecular biology are in need of statistical methods to analyze complex data. We have a
track record of developing such methods and the R35 mechanism will provide us with the flexibility to pivot our
effort as new molecular approaches are being developed.
We will focus on methods for (1) measuring the shared molecular component between experiments, (2) differ-
entiation and cell cycle measurements using single-cell RNA-seq, (3) detecting selection acting on molecular
features such as DNA methylation, histone modifications and transcription factor binding, and (4) epigenomics
assays including nanopore sequencing.

## Key facts

- **NIH application ID:** 10849803
- **Project number:** 5R35GM149323-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Kasper Daniel Hansen
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $462,324
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10849803

## Citation

> US National Institutes of Health, RePORTER application 10849803, Data Science Tools to Increase Insight in Genomics Data (5R35GM149323-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10849803. Licensed CC0.

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