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bioRxiv · 10.1101/2025.08.20.671329

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

Schlegel, L.·Gomez-Cano, F.·Marand, A. P.·Johannes, F.
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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.

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Schlegel, L., Gomez-Cano, F., Marand, A. P., Johannes, F.. 2025-08-24. Polymer-derived distance penalties improve chromatin interaction predictions from single-cell data across crop genomes. https://doi.org/10.1101/2025.08.20.671329

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