# Systems Biology, Bioinformatics, & Data Integration

> **NIH NIH U19** · UNIVERSITY OF WASHINGTON · 2021 · $254,762

## Abstract

Genome-wide research strategies provide unprecedented opportunities for insight but also
major bioinformatic challenges due to the size and complexity of the data. The multidisciplinary
research in this TBRU utilizes cutting-edge research methods that utilize a broad spectrum of
‘omics platforms, including proteomics, genomics (RNA-seq), genome-wide association studies
(GWAS) of the host and pathogen, cellular experimental screens with host and pathogen data,
and targeted model organism experiments. Integration of these datasets and research
strategies requires innovative approaches to mechanistically examine how Mtb and host genetic
variants modulate TB pathogenesis. Core B uses pathway-driven and cutting-edge
bioinformatics approaches to integrate the genetic results from Core A with multiple large-scale
and diverse datasets from each project (proteomics, Path-Seq, RNAseq) to dynamically identify
and prioritize pathways and protein networks for functional testing. While each of these
experiments are analyzed individually within each project, the results have potential for greater
insight beyond each dataset. Core B represents a key source of synergy as data will flow
between all the Projects and Cores and will generate models leading to targeted experiments
with an iterative analytic and hypothesis testing process. This Core will bring together expertise
across the Projects for the different ‘omic platforms as well as bioinformatic strategies for data
integration. Aim 1 provides integrated analyses of the diverse datasets from Core A and each
Project. Aim 2 utilizes network propagation, a systems biology method applied to diverse
disease areas, which uses networks to identify convergent pathways highlighted by distinct
omics-level datasets. This method is useful when the individual gene overlap between studies is
poor, while genes from distinct studies do possess pathway/functional overlap with one another.
Here we apply it to study phenotypic variation in human TB and use it as an independent
method to extract insights and new disease gene targets from the diverse and complex datasets
of this consortium. The overall goal is to generate models from data integration that prioritize
research directions across the Projects and Core A and create testable mechanistic
hypotheses that are iteratively assessed between Core B and all TBRU components.

## Key facts

- **NIH application ID:** 10271171
- **Project number:** 1U19AI162583-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Catherine Marie Stein
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $254,762
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10271171

## Citation

> US National Institutes of Health, RePORTER application 10271171, Systems Biology, Bioinformatics, & Data Integration (1U19AI162583-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10271171. Licensed CC0.

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