# Computational Data Analysis Core

> **NIH NIH P20** · UNIVERSITY OF NORTH DAKOTA · 2021 · $192,321

## Abstract

SUMMARY
In biomedical research, we are witnessing a fundamental transition from a science focused on the function of
single molecules or pathways to information-rich data science that analyzes biological systems and their
behavior as a whole. This approach, known as Systems Biology, integrates data from all levels of complexity
measured by various high-throughput approaches using advanced computational methods to study how
networks of interacting biological components determine the properties and activities of living systems. With
technological advances over the last decade, the ‘Omics’ technologies such as genomics, epigenetics,
transcriptomics, proteomics, and metabolomics, are now incorporated into the everyday methodology of
biological researchers. These high-throughput technologies offer a great opportunity; however, they also pose
great challenges as they rapidly generate large amounts of diverse data and the analysis becomes more
complex. Most biological researchers do not have adequate training in data analysis approaches and
frequently lack access to appropriate databases or software to expedite analysis. Therefore, it is critical to
provide the investigators with high-quality advanced data analysis services as well as training in data analysis
resources to facilitate analysis and interpreting the data.
In this Phase 2 application, we are proposing to establish a new Core, Computational Data Analysis Core
(CDAC), to support the big data analysis needs of the new project leaders and the larger COBRE community.
Three aims are proposed: Aim 1: Establish standardized advanced bioinformatics services for the analysis of
high-throughput Omics data, including but not limited to RNA-Seq, microbiome, and ChIP-Sequencing. Aim 2:
Provide the COBRE and other biomedical researchers with support on comprehensive high-throughput
experimental design, bioinformatics data analysis, technical support, and consultation. Aim 3: Educate faculty,
staff, postdoctoral fellows, and graduate students in computational data analysis technologies. Handling of big
data remains a challenge for most investigators, and adequate training in various tools and analysis pipelines
is critically required for users. The Core will develop and offer hands-on training workshops to help
investigators, students, and postdocs to learn the fundamental steps of data analysis. Educational activities of
the Core will thus help our trainees advance their careers by providing them marketable skills in bioinformatics
data analysis. Overall, CDAC will provide dedicated state-of-the-art and accurate high-throughput data analysis
services and training to the investigators, which will significantly enhance the overall research of HPI COBRE.

## Key facts

- **NIH application ID:** 10270977
- **Project number:** 2P20GM113123-06
- **Recipient organization:** UNIVERSITY OF NORTH DAKOTA
- **Principal Investigator:** Junguk Hur
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $192,321
- **Award type:** 2
- **Project period:** 2016-05-13 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10270977

## Citation

> US National Institutes of Health, RePORTER application 10270977, Computational Data Analysis Core (2P20GM113123-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10270977. Licensed CC0.

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