RCDome: A Multi-Database Human Gene Atlas of Regulated Cell Death
- 1Universidade Estadual do Norte Fluminense Darcy Ribeiro
Abstract
Introduction: Regulated cell death (RCD) governs tissue homeostasis, development, and disease, and is executed or regulated by specific genes. Yet for the researcher asking which genes execute or regulate a given RCD form, the answer is rarely simple: gene lists for the same form rarely agree across databases, many associations carry no traceable source, and most have never been systematically checked against primary literature. We built RCDome to answer the most basic question a researcher can ask: where did this gene-RCD association come from, and what is the evidence? Methods: RCDome is a multi-database human gene atlas built through a staged architecture integrating multi-source compilation, literature validation and evidence recovery, mechanistic classification, human-governed ontological adjudication, and web deployment. Five complementary evidence channels (PubMed Boolean search, curated databases, AI-assisted screening, manual curation, and Elicit systematic review) were integrated from 20 public databases into a unified matrix. Sixty-two original name strings were canonicalized into 53 RCD-related entities with curator-adjudicated operational definitions and ontological levels. Gene-RCD associations were systematically evaluated through an independent DeepSeek two-call LLM validation, Europe PMC full-text recovery and deep evidence extraction, an independent Consensus literature-search channel, and GPT-4o arbitration of disagreements. Source-grounding controls rejected extracted evidence not found verbatim in the source document, while the governance contract was enforced as executable code. Results: The compendium holds 22,473 association rows spanning 9,265 unique HGNC-validated genes (21,568 unique gene-RCD pairs). Evidence-first adjudication resolved 5,138 pairs (23.8%) as mechanistically involved; across the 4,775 role-resolved pairs, 225 (4.7%) are executors, 4,455 (93.3%) are regulators, and 95 are markers, revealing a small death-executing core embedded in a vast regulatory network. Under a separate seven-class mechanistic criterion applied to all 22,473 rows, only 3,063 (13.6%) were mechanistically confirmed and 286 (1.3%) were database-only with no verifiable paper trail. Conclusion: RCDome makes the RCD evidence landscape measurable and traceable, showing that AI-assisted curation can be verifiable, human-governed, and explicit about uncertainty. It is delivered as a Shiny web application that lets any researcher query the atlas and inspect the evidence behind each association. Availability: RCDome is freely available at https://rcdome.org/ (source code and data at https://github.com/BioCancerInformatics/RCDome).
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BibTeX
@misc{souza2026rcdome,
title = {{RCDome: A Multi-Database Human Gene Atlas of Regulated Cell Death}},
author = {E. Rodrigues de Souza and V. dos Santos Lopes and H. Almeida Cordeiro Nogueira and E. Medina-Acosta},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.761548},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.761548v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - RCDome: A Multi-Database Human Gene Atlas of Regulated Cell Death
AU - E. Rodrigues de Souza
AU - V. dos Santos Lopes
AU - H. Almeida Cordeiro Nogueira
AU - E. Medina-Acosta
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.761548
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.761548v1
AB - Introduction: Regulated cell death (RCD) governs tissue homeostasis, development, and disease, and is executed or regulated by specific genes. Yet for the researcher asking which genes execute or regulate a given RCD form, the answer is rarely simple: gene lists for the same form rarely agree across databases, many associations carry no traceable source, and most have never been systematically checked against primary literature. We built RCDome to answer the most basic question a researcher can ask: where did this gene-RCD association come from, and what is the evidence? Methods: RCDome is a multi-database human gene atlas built through a staged architecture integrating multi-source compilation, literature validation and evidence recovery, mechanistic classification, human-governed ontological adjudication, and web deployment. Five complementary evidence channels (PubMed Boolean search, curated databases, AI-assisted screening, manual curation, and Elicit systematic review) were integrated from 20 public databases into a unified matrix. Sixty-two original name strings were canonicalized into 53 RCD-related entities with curator-adjudicated operational definitions and ontological levels. Gene-RCD associations were systematically evaluated through an independent DeepSeek two-call LLM validation, Europe PMC full-text recovery and deep evidence extraction, an independent Consensus literature-search channel, and GPT-4o arbitration of disagreements. Source-grounding controls rejected extracted evidence not found verbatim in the source document, while the governance contract was enforced as executable code. Results: The compendium holds 22,473 association rows spanning 9,265 unique HGNC-validated genes (21,568 unique gene-RCD pairs). Evidence-first adjudication resolved 5,138 pairs (23.8%) as mechanistically involved; across the 4,775 role-resolved pairs, 225 (4.7%) are executors, 4,455 (93.3%) are regulators, and 95 are markers, revealing a small death-executing core embedded in a vast regulatory network. Under a separate seven-class mechanistic criterion applied to all 22,473 rows, only 3,063 (13.6%) were mechanistically confirmed and 286 (1.3%) were database-only with no verifiable paper trail. Conclusion: RCDome makes the RCD evidence landscape measurable and traceable, showing that AI-assisted curation can be verifiable, human-governed, and explicit about uncertainty. It is delivered as a Shiny web application that lets any researcher query the atlas and inspect the evidence behind each association. Availability: RCDome is freely available at https://rcdome.org/ (source code and data at https://github.com/BioCancerInformatics/RCDome).
ER -