biostudies-literatureUnknownGalasso JNIMH NIH HHSNHGRI NIH HHSThe emergence of multiomic single-cell Hi-C methods, which simultaneously profile chromatin conformation and other modalities such as gene expression or DNA methylation, creates tremendous opportunities for studying the genome's structure-function relationships. Existing tools for processing multiomic single-cell Hi-C datasets have certain limitations for downstream bioinformatics analysis. We present map3C, a software tool designed to address these limitations. We demonstrate that map3C improves the quality of multiomic single-cell Hi-C data for analysis and its utility for identifying structural variant locations in the genome.bioRxiv : the preprint server for biology2025.10.10.681728https://www.ebi.ac.uk/biostudies/studies/S-EPMC12640563biostudies-literaturemap3C: a computational tool for processing multiomic single-cell Hi-C data.PMC12640563T32 HG002536U01 HG012079R01 MH125252UM1 HG011593U01 MH130995Galasso JErnst JWang YLuo CAlber Ffalsemap3C: a computational tool for processing multiomic single-cell Hi-C data.The emergence of multiomic single-cell Hi-C methods, which simultaneously profile chromatin conformation and other modalities such as gene expression or DNA methylation, creates tremendous opportunities for studying the genome's structure-function relationships. Existing tools for processing multiomic single-cell Hi-C datasets have certain limitations for downstream bioinformatics analysis. We present map3C, a software tool designed to address these limitations. We demonstrate that map3C improves the quality of multiomic single-cell Hi-C data for analysis and its utility for identifying structural variant locations in the genome.2025-01-01T00:00:00Z2025 Oct2026-05-31T03:10:00.072Z2026-05-31T03:07:31.735ZS-EPMC126405634128777710.1101/2025.10.10.681728