# Data integration and analysis for mapping malaria parasite traits

> **NIH NIH P01** · UNIVERSITY OF NOTRE DAME · 2021 · $188,187

## Abstract

ABSTRACT
The ability to conduct targeted genetic crosses in malaria provides exciting opportunities to uncover genetic
mechanisms linked to drug resistance. Core B will provide a centralized resource for all project data,
standardized bioinformatics analysis, the integrated, network-based analysis required by RP02 and RP03. All
of these data and results will then be deposited in relevant public resources.
Extensive –omics data will be collected for each progeny clone (i.e., transcripts, proteins and metabolites) and
deposited in public archives by Core C. Because such data from the large numbers of unique recombinant
progeny provided by Core A/RP01 are more powerful when integrated, Core B will maintain the required
computational infrastructure to standardize project metadata and link genotypes to collected phenotypes. In
tandem with RP02, Core B serves as the focal point of all project data. Because our
efforts will also generate
specialized metadata for which no standard archives exist, we will work closely with EuPathDB to ensure timely
and relevant community access of all Project data.
Providing standard analysis and significant hardware resources will also open up new avenues of integrated
data analysis within the Project, which in turn will be shared via EuPathDB. Further, these significant, metadata-
labeled collections of data will provide an extremely rich resource for bioinformaticians and computational
biologists. Although publically available datasets of this scope and size are only available for cancer biology,
they have been invaluable in the development of computational methods, e.g., predicting drug combination
efficiency. We imagine our resource would be just as popular and recruit many participants to improve prediction
of drug responses and other biological traits in malaria parasites. In addition to a letter of support from EupathDB
on community access, we also include a letter from African researchers who are already highly interested in this
data resource.

## Key facts

- **NIH application ID:** 10216644
- **Project number:** 5P01AI127338-05
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** Scott Emrich
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $188,187
- **Award type:** 5
- **Project period:** 2017-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10216644

## Citation

> US National Institutes of Health, RePORTER application 10216644, Data integration and analysis for mapping malaria parasite traits (5P01AI127338-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10216644. Licensed CC0.

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