CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data
Abstract
Motivation Chromatin 3D folding creates numerous DNA interactions, participating in gene expression regulation. Single-cell chromatin-accessibility assays now profile hundreds of thousands of cells, challenging existing methods for mapping cis-regulatory interactions. Results We present CIRCE, a fast and scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data. CIRCE re-implements the Cicero workflow to analyse single-cell atlases, cutting runtime and memory use by several orders of magnitude. We also provide new options to compute metacells, grouping similar cells to reduce data sparsity. We benchmarked CIRCE against Cicero on two datasets of different sizes and demonstrated the improvement from CIRCE’s metacells’ strategy with promoter capture Hi-C data. We also evaluated how DNA interaction predictions are impacted by different pre-processing. We observed a negative impact of Cicero’s count normalization, and the best performance was obtained with the single-cell count matrix directly. Finally, we demonstrated the scalability of CIRCE by processing a dataset of more than 700 000 cells and 1 million DNA regions in less than an hour. CIRCE should greatly facilitate the prediction of DNA region interactions for scverse and Python users, while providing new and up-to-date pre-processing insights.
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BibTeX
@article{trimbour2026circe,
title = {{CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data}},
author = {Rémi Trimbour and Julio Saez-Rodriguez and Laura Cantini},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag092},
url = {https://hal.science/hal-05797131v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data
AU - Rémi Trimbour
AU - Julio Saez-Rodriguez
AU - Laura Cantini
PY - 2026
DA - 2026/10/13
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag092
UR - https://hal.science/hal-05797131v1
AB - Motivation Chromatin 3D folding creates numerous DNA interactions, participating in gene expression regulation. Single-cell chromatin-accessibility assays now profile hundreds of thousands of cells, challenging existing methods for mapping cis-regulatory interactions. Results We present CIRCE, a fast and scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data. CIRCE re-implements the Cicero workflow to analyse single-cell atlases, cutting runtime and memory use by several orders of magnitude. We also provide new options to compute metacells, grouping similar cells to reduce data sparsity. We benchmarked CIRCE against Cicero on two datasets of different sizes and demonstrated the improvement from CIRCE’s metacells’ strategy with promoter capture Hi-C data. We also evaluated how DNA interaction predictions are impacted by different pre-processing. We observed a negative impact of Cicero’s count normalization, and the best performance was obtained with the single-cell count matrix directly. Finally, we demonstrated the scalability of CIRCE by processing a dataset of more than 700 000 cells and 1 million DNA regions in less than an hour. CIRCE should greatly facilitate the prediction of DNA region interactions for scverse and Python users, while providing new and up-to-date pre-processing insights.
SN - 1367-4803
SN - 1367-4811
ER -