# Data Management and Analysis Core

> **NIH NIH P42** · UNIVERSITY OF ARIZONA · 2022 · $244,461

## Abstract

PROJECT SUMMARY (Data Management and Analysis Core: Aikseng Ooi and Nirav Merchant) 
The University of Arizona Superfund Research Program (UA SRP) will generate volumes and types of data that 
are not manageable in traditional laboratory settings. The Data Management and Analysis Core (DMAC) will 
function as the primary service for UA SRP into large biological, geophysical, and chemical datasets, including 
but not limited to RNA sequencing, chromatin immunoprecipitation sequencing, exome sequencing, 
metabolomics, metagenomics, microbiome amplicon sequencing, geospatial positioning, analytical chemistry, 
and imaging. DMAC enables investigators by performing three core functions: (1) DMAC will lead the housing 
of all data in an easy-to-access data repository system: CyVerse. Cyverse is a computational infrastructure 
consisting of hardware, software, and personnel that are designed to handle huge datasets and complex 
analyses, and is maintained at the University of Arizona. DMAC will utilize a reference implementation (RI) that 
divides data into five different levels for easy data sharing, processing, and analyzing. Lowest levels (level 1) 
will be raw data, while higher levels (level 5) will be file formats utilizable in graphics visualizations. DMAC will 
support these processes with help from on-staff statisticians and bioinformaticians who can devise analysis 
strategies for individual investigators. In addition to data storage, DMAC will orchestrate sample management 
using Fulcrum software. Fulcrum allows barcoding, global positioning, and annotation of biological samples in 
an easy-to-use application available on both traditional workstations and mobile platforms. Fulcrum is critical for 
point-of-generation sample tracking due to its mobility. (2) Beyond data and sample management, DMAC will 
perform both standard and custom computational analyses of the data. This will include DMAC-lead 
investigations into “feature signatures”, which address the predictability of data across UA SRP projects; for 
example, can the gene expression changes associated with a particular arsenic treatment predict metagenomics 
changes in a similarly treated sample? In conjunction with UA SRP investigators, DMAC will apply traditional 
algorithms, or develop novel algorithms as needed, to identify signatures for the different data types collected. 
(3) The storage and analytical capabilities of DMAC will be integrated into a user-friendly web application that 
allows individual investigators to retrieve, manipulate, and visualize UA SRP data. The web application will be 
implemented using an in-house maintained server in conjunction with the R statistical environment. DMAC is 
thus an integral component of the UA SRP proposal that utilizes state-of-the-art technologies to enable the 
discovery of novel insights into arsenic exposure and its role in health and disease.

## Key facts

- **NIH application ID:** 10337256
- **Project number:** 5P42ES004940-33
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Aikseng Ooi
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $244,461
- **Award type:** 5
- **Project period:** 1997-04-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10337256

## Citation

> US National Institutes of Health, RePORTER application 10337256, Data Management and Analysis Core (5P42ES004940-33). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10337256. Licensed CC0.

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