Research Goals

1. Integrating Deep Learning and Single-Molecule Sequencing for rapid multiomics
My research applies deep learning to single-molecule sequencing data, building predictive models that help researchers and clinicians understand how genetic variation drives disease in a faster, more complete, and more scalable manner.

Integrating Single-Molecule Sequencing and Deep Learning to Predict Haplotype-Specific 3D Chromatin Organization in a Mendelian Condition.     Dubocanin D,  … Altemose N.  bioRxiv (2025). 

2. Clinical telomere-to-telomere genomics
I develop and apply methods to study how the genome is maintained and regulated in health and disease, probing genomic regulation and its breakdown across the entire genome, telomere to telomere, in diseases like cancer.

A telomere-to-telomere map of somatic mutation burden and functional impact in cancer. Sohn M-H*, Dubocanin D*,... Stergachis AB. bioRxiv (2025).

3. Uncovering Function and Regulation of Repetitive DNA and the "Dark Genome"
Repetitive DNA and other poorly understood genomic regions, often called the genome's "dark matter", remain a vast, under-explored frontier. I study how these elements shape genome organization, gene regulation, and stability, working to untangle their sequence complexity and the layered ways they impact human biology.

Conservation of dichromatin organization along regional centromeres.  Dubocanin D, … Stergachis AB. Cell Genomics (2025).

Single-molecule architecture and heterogeneity of human telomeric DNA and chromatin. Dubocanin D, … Altemose N, Stergachis AB bioRxiv (2026). 

A telomere-to-telomere map of somatic mutation burden and functional impact in cancer. Sohn M-H*, Dubocanin D*,... Stergachis AB. bioRxiv (2025).