map3C: a computational tool for processing multiomic single-cell Hi-C data

Abstract Summary 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. Competing Interest Statement The authors have declared no competing interest. Footnotes Availability and Implementation: https://github.com/luogenomics/map3C Contact: Jason Ernst (jason.ernst{at}ucla.edu), Chongyuan Luo (cluo{at}mednet.ucla.edu) Supplementary information: supplementary_data.docx