Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis
- 1University of California Irvine
Abstract
MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference with a comprehensive differential testing suite. Execution is containerized, tracks provenance, robust to interruption, optimized for cluster-based compute environments, and allows for nuanced customization of specific processes. SUMMARY Single-nucleus multiome assays jointly profile gene expression and chromatin accessibility, yet their analysis typically requires bespoke chaining of modality-specific tools, creating barriers to reproducibility, scalability, and regulatory interpretation. We present FORGE (Flow Orchestrated Regulatory Genomics Engine), a configurable workflow that automates standalone snRNA-seq and snATAC-seq analysis, integrates the pair through complementary linear and nonlinear latent-variable models, and carries them through regulatory-network inference and differential testing. We evaluated FORGE on four human and mouse datasets spanning blood, brain, and kidney and two multiome chemistries, including a twelve-sample CRND8 Alzheimers disease cohort. We report cross-modal agreement alongside missing-modality reconstruction and an accounting of computational cost. In the Alzheimer's cohort, FORGE nominated a glial Mef2c-associated program defensible across expression, co-accessibility, footprinting, and eRegulon evidence. In human PBMC, FORGE layered evidence models also provide nuanced interpretations that largely corroborate previously published regulatory links while also proposing an additional CD83 myeloid module.
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BibTeX
@misc{solano2026flow,
title = {{Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis}},
author = {L. E. Solano and N. Rahimzadeh and Z. Shi and V. Swarup},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.10.750690},
url = {https://www.biorxiv.org/content/10.64898/2026.09.10.750690v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis
AU - L. E. Solano
AU - N. Rahimzadeh
AU - Z. Shi
AU - V. Swarup
PY - 2026
DA - 2026/09/10
PB - bioRxiv
DO - 10.64898/2026.09.10.750690
UR - https://www.biorxiv.org/content/10.64898/2026.09.10.750690v1
AB - MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference with a comprehensive differential testing suite. Execution is containerized, tracks provenance, robust to interruption, optimized for cluster-based compute environments, and allows for nuanced customization of specific processes. SUMMARY Single-nucleus multiome assays jointly profile gene expression and chromatin accessibility, yet their analysis typically requires bespoke chaining of modality-specific tools, creating barriers to reproducibility, scalability, and regulatory interpretation. We present FORGE (Flow Orchestrated Regulatory Genomics Engine), a configurable workflow that automates standalone snRNA-seq and snATAC-seq analysis, integrates the pair through complementary linear and nonlinear latent-variable models, and carries them through regulatory-network inference and differential testing. We evaluated FORGE on four human and mouse datasets spanning blood, brain, and kidney and two multiome chemistries, including a twelve-sample CRND8 Alzheimers disease cohort. We report cross-modal agreement alongside missing-modality reconstruction and an accounting of computational cost. In the Alzheimer's cohort, FORGE nominated a glial Mef2c-associated program defensible across expression, co-accessibility, footprinting, and eRegulon evidence. In human PBMC, FORGE layered evidence models also provide nuanced interpretations that largely corroborate previously published regulatory links while also proposing an additional CD83 myeloid module.
ER -