BEAR-GRN: Systematic assessment of single-cell multi-omics-based gene regulatory network inference methods.
- 1Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, USA.
- 2Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, USA. [email protected].
- 3Department of Molecular and Precision Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA. [email protected].
Abstract
Identifying regulatory gene interactions is inherently challenging as it depends on both the transcriptional and epigenetic landscapes. Multiple methods are proposed to infer gene regulatory networks (GRNs) using single-cell multi-omics data, yet the absence of a standardized benchmarking framework remains an obstacle to unbiased performance evaluation. Here we present BEAR-GRN, a systematic resource providing standardized datasets, curated ground-truth networks, and a unified computational pipeline for benchmarking GRN inference methods. Using BEAR-GRN, we assess state-of-the-art multi-omics GRN inference methods across paired single-cell RNA-seq and ATAC-seq datasets spanning human and mouse cell types. We evaluate accuracy against four complementary ground-truth definitions, alongside computational efficiency, output stability, and modality contribution. Although ground-truth choice profoundly influences performance, LINGER and DIRECT-NET show the highest overall accuracy and stability. Modality perturbation experiments reveal that current methods are predominantly RNA-driven, with chromatin accessibility contributing limited independent signal. BEAR-GRN enables researchers to evaluate existing and new GRN methods against established baselines.
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BibTeX
@article{karamveer2026beargrn,
title = {{BEAR-GRN: Systematic assessment of single-cell multi-omics-based gene regulatory network inference methods.}},
author = {Karamveer Karamveer and Eric Moeller and Hannah Valensi and Ewura-Esi Manful and Yasin Uzun},
journal = {Nature communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77838-w},
url = {https://europepmc.org/article/MED/42901905},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - BEAR-GRN: Systematic assessment of single-cell multi-omics-based gene regulatory network inference methods.
AU - Karamveer Karamveer
AU - Eric Moeller
AU - Hannah Valensi
AU - Ewura-Esi Manful
AU - Yasin Uzun
PY - 2026
DA - 2026/09/18
JO - Nature communications
DO - 10.1038/s41467-026-77838-w
UR - https://europepmc.org/article/MED/42901905
AB - Identifying regulatory gene interactions is inherently challenging as it depends on both the transcriptional and epigenetic landscapes. Multiple methods are proposed to infer gene regulatory networks (GRNs) using single-cell multi-omics data, yet the absence of a standardized benchmarking framework remains an obstacle to unbiased performance evaluation. Here we present BEAR-GRN, a systematic resource providing standardized datasets, curated ground-truth networks, and a unified computational pipeline for benchmarking GRN inference methods. Using BEAR-GRN, we assess state-of-the-art multi-omics GRN inference methods across paired single-cell RNA-seq and ATAC-seq datasets spanning human and mouse cell types. We evaluate accuracy against four complementary ground-truth definitions, alongside computational efficiency, output stability, and modality contribution. Although ground-truth choice profoundly influences performance, LINGER and DIRECT-NET show the highest overall accuracy and stability. Modality perturbation experiments reveal that current methods are predominantly RNA-driven, with chromatin accessibility contributing limited independent signal. BEAR-GRN enables researchers to evaluate existing and new GRN methods against established baselines.
SN - 2041-1723
ER -