# Predictive models of boundaries of topologically associated domains (TADs)

> **NIH NIH R15** · UNIVERSITY OF HOUSTON-DOWNTOWN · 2020 · $399,888

## Abstract

Predictive Models of Boundaries of Topologically Associated Domains (TADs) in Human.
The PI proposes a high-impact research project to develop novel predictive models to predict boundaries of
topologically associated domains in a variety of human cell lines. The goal of this proposal is not just building
these models, but to interpret them to decipher some biological meaning. Studies have recently indicated that
genomes of different organisms are organized into domains called topologically associated domains (TADs),
which consist of self-interacting chromatin regions. The boundaries of TADs have been linked to several diseases
and gene regulation by modulating the overall genome organization. Accurate identification of these boundaries
and interpretation of regulatory units in the boundaries are essential for development of effective therapy for
diseases in future. Since existing experimental methods are costly and challenging, we will explore, evaluate, and
compare predictive models for accurate prediction of TAD boundaries in different cell lines. We propose to test
if sequence and chromatin features can be used in traditional feature based predictive models for TADs
boundaries. This will be compared to deep learning models which have the capacity to “learn” important
discriminative features from sequence information only. By interpreting these models, we will characterize the
regulatory code of TAD boundaries by (a) characterizing a comprehensive list of motifs in TAD boundaries in 10
different human cell lines, (b) characterizing the association between different chromatin features and their
predicting power of the boundaries, and (c) scoring, ranking and prioritizing likely causal noncoding variants that
occur in TAD boundaries. Furthermore, this proposal will enhance the infrastructure of research and education
at University of Houston-Downtown, introducing computational biology research experiences to
underrepresented minority, first time in college and female undergraduate students, who would otherwise lack
such opportunities. This will allow them to acquire a broad area of skills in data analytics, critical thinking, and
research methods, which will encourage them to pursue a biomedical career and change their lives and
communities.

## Key facts

- **NIH application ID:** 9965292
- **Project number:** 1R15GM137254-01
- **Recipient organization:** UNIVERSITY OF HOUSTON-DOWNTOWN
- **Principal Investigator:** Benjamin Soibam
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $399,888
- **Award type:** 1
- **Project period:** 2020-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9965292

## Citation

> US National Institutes of Health, RePORTER application 9965292, Predictive models of boundaries of topologically associated domains (TADs) (1R15GM137254-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9965292. Licensed CC0.

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