ArticleNAR genomics and bioinformatics2026
Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.
Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 multiregime power-law exponents. Applying these corrections to single-cell ATAC sequencing 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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