# CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data

**Authors:** Rémi Trimbour, Julio Saez-Rodriguez, Laura Cantini

PMC · DOI: 10.1093/bioinformatics/btag092 · Bioinformatics · 2026-02-24

## TL;DR

CIRCE is a fast Python tool for predicting DNA interactions from large-scale single-cell chromatin data, improving speed and scalability over existing methods.

## Contribution

CIRCE introduces a scalable and efficient implementation with new metacell computation options for cis-regulatory interaction prediction.

## Key findings

- CIRCE reduces runtime and memory use by several orders of magnitude compared to Cicero.
- Using the raw single-cell count matrix yields better performance than Cicero’s normalized counts.
- CIRCE processed over 700,000 cells and 1 million DNA regions in under an hour.

## Abstract

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.

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.

CIRCE is released as an open-source software under the AGPL-3.0 licence. The package source code is available on GitHub at https://github.com/cantinilab/CIRCE, and its documentation is accessible at https://circe.readthedocs.io. The code to reproduce the presented results is available as a Snakemake pipeline at https://github.com/cantinilab/circe_reproducibility.s.

## Full-text entities

- **Genes:** PC (pyruvate carboxylase) [NCBI Gene 5091] {aka PCB}
- **Diseases:** PC (MESH:D015324), HPC (MESH:C537243)
- **Species:** Mus musculus (house mouse, species) [taxon 10090], Homo sapiens (human, species) [taxon 9606], Drosophila melanogaster (fruit fly, species) [taxon 7227]

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## References

43 references — full list in the complete paper: https://tomesphere.com/paper/PMC12987762/full.md

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Source: https://tomesphere.com/paper/PMC12987762