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Mapping Mammalian 3D Genome Interactions with Micro-C-XL
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Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees.

Colin M Brand1,2, Shuzhen Kuang3, Erin N Gilbertson1,4

  • 1Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.

Genome Biology and Evolution
|October 9, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models trained on human genome data predict 3D genome folding in bonobos and chimpanzees. This reveals species-specific patterns and genetic variants potentially driving phenotypic differences in primates.

Keywords:
3D genome foldingbonobochimpanzeegene regulationmachine learning

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Computational Biology

Background:

  • The three-dimensional (3D) genome structure is crucial for regulating gene expression and influencing phenotypic traits.
  • Understanding species differences requires comparing 3D genome organization, but data is scarce for non-human primates.
  • Human experimental data offers a potential resource to bridge this gap.

Purpose of the Study:

  • To develop and apply a machine learning approach to predict 3D genome contacts in species with limited experimental data.
  • To investigate species-specific patterns of genome folding and 3D divergence between bonobos and chimpanzees.
  • To identify genetic variants associated with observed differences in genome structure and their potential role in phenotypic divergence.

Main Methods:

  • Utilized a machine learning model trained to predict 3D genome contacts from DNA sequence.
  • Applied the model to the genomes of 56 bonobos and chimpanzees, leveraging human experimental data.
  • Analyzed predicted 3D contact maps to estimate genome folding divergence across individuals and species.

Main Results:

  • Identified species-specific patterns of genome folding in bonobos and chimpanzees.
  • Estimated substantial 3D divergence in approximately 17% of analyzed genomic windows.
  • Discovered 89 divergent windows between bonobos and chimpanzees, overlapping genes linked to phenotypic traits, and identified 51 bonobo-specific variants potentially explaining these folding patterns.

Conclusions:

  • Machine learning can effectively use human data to infer 3D genome organization in other species, filling critical data gaps.
  • This study provides the first population-level view of 3D genome variation in non-human primates.
  • Identified genomic loci with altered 3D folding that may underlie phenotypic differences in closely related species.