Polymer-derived distance penalties improve chromatin interaction predictions from single-cell data across crop genomes

Abstract Scalable proxies for 3D genome contacts - such as single-cell co-accessibility and deep learning predictions - have emerged as powerful alternatives to chromatin capture-based methods, but predictions systematically overestimate long-range interactions. Here we show how to correct this bias using distance-based penalty functions informed by Gaussian mixture modeling and polymer-physics scaling. Using Hi-C datasets from maize, rice, and soybean, we derive tissue-specific and global consensus penalties parameterized by multi-regime power-law exponents. Applying these corrections to scATAC-seq co-accessibility scores improves their distance profiles in concordance with Hi-C and reduces long-range false positives by an average of 73% with tissue-specific penalties and 66% with the global consensus. We provide open-source code and fitted parameters to support adoption in maize, rice, and soybean. Competing Interest Statement The authors have declared no competing interest. Footnotes This version has been revised to include a finalized multi-component power-law penalty framework and expanded validation across soybean and rice datasets. Figures 2 and 3 have been updated.. Data and code availability sections were added to include GitHub and Zenodo repositories. Supplemental files, including Table S3 and penalty parameters, have been updated.