Chromosome-scale assembly of yeast genomes using enzyme-free, dense optical genome mapping
Abstract
Large repetitive elements and complex chromosomal organization continue to challenge accurate assembly of eukaryotic genomes. Optical genome mapping (OGM) provides long-range genomic information by imaging individual DNA molecules. While most OGM protocols rely on sparse enzymatic labeling that restricts resolution in poorly labeled or structurally complex regions, dense labeling strategies generate continuous fluorescence intensity profiles that offer a complementary representation of genome structure. Here we extend our Dense Optical Genome Mapping Assembly (DOGMA) pipeline, previously used for bacterial genomes, to eukaryotic genomes, exemplified by the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe , with compact, yet structurally rich genomes. We use a competitive binding (CB)-based dense OGM protocol that produces continuous intensity profiles, reflecting local AT/GC-content, along individual DNA molecules. By explicitly accounting for structural features inherent to eukaryotic genomes that complicate assembly, DOGMA reconstructs genome-wide optical maps while avoiding collapse of non-adjacent repetitive loci. The resulting chromosome-scale assemblies span the genomes of both yeasts and show local differences relative to the corresponding reference genomes, including expanded repetitive arrays ( > 500 kbp). These results establish a comprehensive framework for scalable dense OGM to increasingly complex genomes, including the human genome, where we foresee that it can be efficiently used for disease diagnosis.
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BibTeX
@article{lealgarza2026chromosomescale,
title = {{Chromosome-scale assembly of yeast genomes using enzyme-free, dense optical genome mapping}},
author = {Luis Mario Leal-Garza and Hanna Zachrisson and Albertas Dvirnas and Gaurav Goyal and Ikenna Obi and Sriram KK and Nasim Sabouri and Tobias Ambjörnsson and Fredrik Westerlund},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78211-7},
url = {https://www.nature.com/articles/s41467-026-78211-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Chromosome-scale assembly of yeast genomes using enzyme-free, dense optical genome mapping
AU - Luis Mario Leal-Garza
AU - Hanna Zachrisson
AU - Albertas Dvirnas
AU - Gaurav Goyal
AU - Ikenna Obi
AU - Sriram KK
AU - Nasim Sabouri
AU - Tobias Ambjörnsson
AU - Fredrik Westerlund
PY - 2026
DA - 2026/10/01
JO - Nature Communications
DO - 10.1038/s41467-026-78211-7
UR - https://www.nature.com/articles/s41467-026-78211-7
AB - Large repetitive elements and complex chromosomal organization continue to challenge accurate assembly of eukaryotic genomes. Optical genome mapping (OGM) provides long-range genomic information by imaging individual DNA molecules. While most OGM protocols rely on sparse enzymatic labeling that restricts resolution in poorly labeled or structurally complex regions, dense labeling strategies generate continuous fluorescence intensity profiles that offer a complementary representation of genome structure. Here we extend our Dense Optical Genome Mapping Assembly (DOGMA) pipeline, previously used for bacterial genomes, to eukaryotic genomes, exemplified by the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe , with compact, yet structurally rich genomes. We use a competitive binding (CB)-based dense OGM protocol that produces continuous intensity profiles, reflecting local AT/GC-content, along individual DNA molecules. By explicitly accounting for structural features inherent to eukaryotic genomes that complicate assembly, DOGMA reconstructs genome-wide optical maps while avoiding collapse of non-adjacent repetitive loci. The resulting chromosome-scale assemblies span the genomes of both yeasts and show local differences relative to the corresponding reference genomes, including expanded repetitive arrays ( > 500 kbp). These results establish a comprehensive framework for scalable dense OGM to increasingly complex genomes, including the human genome, where we foresee that it can be efficiently used for disease diagnosis.
SN - 2041-1723
ER -