# Bioinformatics, Data Analytics and Predictive Modeling

> **NIH NIH P30** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2021 · $301,819

## Abstract

The overarching goal of the Bioinformatics, Data Analytics and Predictive Modeling Core is to support our users'
informatics needs and to create innovative informatics resources for the addiction and neuroscience fields to
advance our understanding of the cell-cell signaling, drug addiction and drug exposure processes. High-
throughput peptidomics, proteomics, metabolomics, transcriptomics, and genomics studies advance our
understanding of the molecular processes associated with health and disease, and encompass sequencing,
identification, and profiling of peptides, proteins, protein assemblies, metabolites, transcripts, and genes. The
integration of this information strengthens the characterization of molecular pathways underlying the effects of
exposure to drugs of abuse. Challenges arise from examining complex mixtures of proteins and peptides, and
from analyzing massive volumes of data from peptides, protein and transcript isoforms, and genes. These
challenges are magnified when studying neuropeptides because of their complex post-transcriptional and post-
translational processing. Studying neurotransmitters, proteins, protein complexes, and metabolites present
comparable challenges. Our Core's steadfast mission is to develop and facilitate the use of robust and sensitive
analytical tools to advance the understanding of molecular processes associated with drug exposure and cell
signaling. We have become a premier resource for the annotation, prediction, and characterization of
neuropeptides and proteoforms while sharing our findings through public repositories and open source discovery
tools. Our mission is accomplished by (a) developing needed bioinformatics resources; and (b) collaborating with
the Sampling and Separation Core and the Molecular Profiling and Characterization Core in assisting users on
bioinformatics matters, including experimental design; advanced analysis, benchmarking, and cross-validation;
and visualization of the findings. Addressing present bioinformatic limitations, four Aims are proposed: (1)
rigorous identification of peptides and proteins; (2) precise detection and quantification of the peptides linked to
cell-cell signaling; (3) enhanced characterization of driver molecules and molecular relationships associated with
cell signaling and drug exposure; and (4) precise characterization and quantification of the proteoforms and
protein complexes linked to cell-cell signaling and dug exposure. The significance of the proposed efforts centers
on the development of bioinformatics resources that will improve the accuracy and precision of peptide, protein,
proteoform, protein complex, metabolite, and transcript identification and characterization through the integration
of multi-omic information. The developed approaches will be applied to augment our understanding of addiction-
associated pathways and shared with the neuroscience community.

## Key facts

- **NIH application ID:** 10180926
- **Project number:** 5P30DA018310-18
- **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:** 2021
- **Award amount:** $301,819
- **Award type:** 5
- **Project period:** 2004-08-23 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10180926

## Citation

> US National Institutes of Health, RePORTER application 10180926, Bioinformatics, Data Analytics and Predictive Modeling (5P30DA018310-18). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10180926. Licensed CC0.

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