# Bioinformatics, Data Analytics and Predictive Modeling

> **NIH NIH P30** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2024 · $351,423

## Abstract

The Bioinformatics, Data Analytics and Predictive Modeling Core has two primary objectives: i) to assist the
Center users with bioinformatics, statistical, and programming needs, and ii) to develop and facilitate access to
insightful analytical and predictive resources that enable the mining of high-throughput transcriptomic, proteomic,
and metabolomic experiments. Working closely with the Sampling and Separation and Molecular Profiling and
Characterization Cores, our data analyses enhance the understanding of cell-cell signaling mechanisms
associated with drug abuse and pain perception. The Bioinformatics Core stands out as a global leader in
annotating neuropeptide genes and their corresponding peptide products, and developing web services that
support research on neuropeptide, proteoform, and protein complex identification, quantification, and annotation.
Core products include the development and continuous update of comprehensive catalogs of neuropeptide
genes and products, proteoforms, and metabolites. The Bioinformatics Core’s impact is profound, as evidenced
by the number of publication citations and active utilization of the core's web services, including NeuroPred,
neuroProSight, PepShop, TDPortal, and the Human and Mouse Brain Proteoform Atlases, demonstrating the
ongoing demand for our bioinformatic and analytical services. Moving forward, the primary mission of this core
remains the support of Center users in the design and analysis of data from high-throughput ‘omic experiments
planned to test hypotheses about molecular processes associated with cell-cell signaling, drug abuse, and pain
perception. To address existing gaps in high-throughput ‘omic research of the molecular processes underlying
cell-cell signaling, drug abuse, and pain perception, the Bioinformatics Core has outlined the following specific
aims: 1) advance the understanding of targeted molecules and biomarkers through multi-omics analysis at both
single-cell and aggregate level supported by user-friendly web services; 2) assist preclinical breakthroughs by
providing enriched annotation of key molecules, their isoforms, and networks; 3) precisely characterize
proteoforms and protein complexes; and 4) prepare and empower junior researchers to employ bioinformatics
resources through educational materials and training. The proposed activities address the surge in demand for
artificial intelligence-guided analysis of ‘omic data driven by advancements that yield vast amounts of
information, such as the transition from bulk to single-cell and spatial transcriptomics, epigenomics, proteomics,
and metabolomics. Our core excels in developing bioinformatics pipelines that integrate information across
platforms and employs Big Data to Knowledge approaches to capture multi-omic data signals and variability,
and sift through noise. The Bioinformatics Core’s innovation hinges on facilitating the exploration of transcript,
peptide, and metabolic isoforms that significantly impact dru...

## Key facts

- **NIH application ID:** 10934862
- **Project number:** 2P30DA018310-21
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** SANDRA L RODRIGUEZ ZAS
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $351,423
- **Award type:** 2
- **Project period:** 2004-08-23 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10934862

## Citation

> US National Institutes of Health, RePORTER application 10934862, Bioinformatics, Data Analytics and Predictive Modeling (2P30DA018310-21). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10934862. Licensed CC0.

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