# Coevolution and Functional Interactions in the Non-Coding Genome

> **NIH NIH R35** · NORTHEASTERN UNIVERSITY · 2024 · $397,000

## Abstract

Project Summary / Abstract
A central problem in biology is to understand how genomic variation affects genome function to influence
phenotypes. Key challenges and opportunities lie in linking genomic variants to phenotypes, human health, and
disease. Because it is not feasible to experimentally probe all genomic variants of interest in all contexts,
improved computational methods to accurately predict the impact of unknown genomic variants are necessary.
The aim of this research proposal is to gain mechanistic understanding of functional genomic interactions and
ultimately to develop computational approaches to model and predict relationships among variation, functional
elements, genome function, and phenotype. Two recently acquired key assets will be used to infer distal
functional interactions among DNA elements: i) 3D genomics data and ii) multiple genome alignments. High-
resolution contact mapping experiments (Hi-C and similar methods) have shown that the structural ensembles
of chromosomes are fluid and yet specific to cell type and phase of life1. These ensembles of partially organized
structures bring sections of DNA separated by great genomic distance into close spatial proximity and play an
important role in controlling gene transcription 2,3. By measuring the frequency of physical contacts among DNA
elements, DNA-DNA proximity ligation assays offer insight into the existence of functional interactions among
the same elements, even when the nature of the interaction is unknown. In the last few years, there has been
an explosion of activity directed toward assembling the genomes of many species 35–37. Hundreds of newly
assembled end-to-end genomes constitute a dataset of transformative importance in studying the general
operating principles of genomes across the tree of life using evolutionary information. This proposal aims to
combine data from proximity ligation assays and coevolutionary information extracted from multiple genome
alignments to infer the network of functional interactions among DNA elements. The computational approach will
be based on Direct Coupling Analysis 29–32 (DCA) and other machine learning methods. The PI has previously
employed DCA to study genome architecture 33 as well as in other contexts 34, and has already made important
contributions to the field of 3D genomics.

## Key facts

- **NIH application ID:** 10874702
- **Project number:** 5R35GM146852-03
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** Michele Di Pierro
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $397,000
- **Award type:** 5
- **Project period:** 2022-09-22 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874702

## Citation

> US National Institutes of Health, RePORTER application 10874702, Coevolution and Functional Interactions in the Non-Coding Genome (5R35GM146852-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10874702. Licensed CC0.

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