Welcome to AgingHallmarksDB

Aging is a complex biological process driven by the interplay of multiple genetic and molecular factors. Understanding the genetic basis of aging is essential for advancing research in health, longevity, and age-related diseases. The hallmarks framework, originally proposed by Carlos López-Otín and colleagues in 2013 and later updated in 2023, provides a widely accepted conceptual structure for systematically organizing the molecular and cellular alterations underlying aging hallmarks.

Here, we present AgingHallmarksDB, an interactive web platform that systematically catalogs and curates genes associated with the 11 of 12 hallmarks of aging through the integration of multiple established biological databases. In addition to hallmark-specific gene annotations, the platform provides tissue specificity, cell-type classifications, exosome-associated expression, transcription factor (TF)–target interactions, and kinase–substrate relationships, enabling a systems-level understanding of aging biology. AgingHallmarksDB further supports hallmark enrichment analysis of user-submitted gene lists and offers interactive network visualizations to explore the regulatory and signalling landscapes underlying aging-associated molecular processes across tissues and cellular systems.

AgingHallmarksDB Overview

Browse by Aging Hallmarks

CITATION

If you use our resource, please cite the following research article:

Rahul Tiwari, Mridhula Balaji, Nikhil Chivukula, Priyotosh Sil, Areejit Samal*, An integrated resource for systems-level analysis of aging hallmarks and associated genes, bioRxiv, 2026.05.29.728838 (2026).

* Corresponding author

📥 Download Table

Filter Selection

Hallmark Enrichment

Identify significantly enriched aging hallmarks within the user-provided gene set.

1 Enter gene list

⚠️ Critical GSEA Requirement: You must input your entire, unfiltered transcriptome (all measured genes, typically 15,000+) ranked by their expression metric (e.g., fold-change). Submitting a small, pre-filtered list of only significant genes breaks the algorithm's statistical background model and yields invalid results.

2 Select parameters

3 Submit

Hallmark enrichment analysis results

GSEA Enrichment Plot

Gene Annotation

Retrieve comprehensive annotations for the user-provided gene set across aging hallmarks, tissues, cell types, and presence in exosomes.

1 Enter gene list

2 Select parameters

3 Submit

Gene Annotation Results


Topological Analysis of Hallmark-Associated PPI Networks

Identify important hallmark-associated genes based on protein-protein interaction network topological measures, including degree, betweenness centrality, and closeness centrality.

1 Enter gene list

2 Select parameters

3 Submit


Transcription Factor-Target Interactions

The tool maps transcription factor (TF)-target interactions among the user-provided gene set, specifically focusing on aging-associated genes curated within this resource.

1 Enter gene list

2 Select parameters

3 Submit


Kinase-Substrate Interactions

The tool maps kinase-substrate interactions among the user-provided gene set, specifically focusing on aging-associated genes curated within this resource.

1 Enter gene list

2 Select parameters

3 Submit


Download Database Content

Filter and download a custom subset of the AgingHallmarksDB using the parameters below.
Download TSV
ACKNOWLEDGEMENT
We would like to thank the authors of the following resources which were used to build AgingHallmarksDB.
PurposeResource
Source for Aging Associated Genes
Single Cell Data
Gene Identifier Mapping
TF-Target and Kinase-Substrate interaction
Exosome Mapping
Enrichment computation
Web Interface
FUNDING

Research in the group of Areejit Samal at The Institute of Mathematical Sciences (IMSc), Chennai is financially supported by the Department of Atomic Energy (DAE), Government of India. The funders have no role in study design, prediction, analysis or decision to publish this work.

Help & Tutorial

1. Browse

The Browse page offers a comprehensive overview of the 11 of 12 recognized hallmarks of aging. By clicking on any hallmark icon, users can dynamically generate a detailed table of its associated genes. These results can be further refined by applying stringent evidence-based filters or by restricting the search to specific tissues and cell-type classes.

2. Hallmark Enrichment

The Hallmark Enrichment module allows users to perform hallmark enrichment analysis using two distinct approaches: Standard Overlap (ORA) by submitting a simple list of Entrez Gene IDs, or Ranked GSEA by providing a list of genes with associated ranking metrics (such as log fold-change). Users can refine their analysis by selecting parameters such as gene set type, evidence stringency, and specific tissue or cell-type filters. After clicking “Run Analysis”, an interactive results dashboard is generated. This dashboard displays enriched hallmarks along with their statistical significance (adjusted p-values) and, for GSEA, Normalized Enrichment Scores (NES). These results can be further filtered based on user-defined minimum overlap and significance thresholds. Users can visualize ORA and GSEA results using highly customizable bar plots, bubble plots, or circular plots, while GSEA results further feature dedicated, hallmark-specific enrichment (barcode) plots. All generated plots can be downloaded directly in high-resolution PDF or PNG formats.

3. Gene Annotation

The Gene Annotation tool allows user to rapidly extract structured metadata for a customized list of Entrez Gene IDs. Upon submission, the platform redirects to a summary overview followed by an annotated table. This table maps the input genes to their respective aging hallmarks and details their spatial expression profile, including their documented presence across various tissues, specific cell types, and exosome datasets.

4. PPI Network Topology

The PPI Network Topology module provides protein-protein interaction-based topological analysis of hallmark-associated genes using degree, betweenness centrality, and closeness centrality to identify key network nodes.

5. Regulatory Interactions

The Regulatory Interactions module offers advanced topological mapping for user-defined gene sets, visualizing key biological interaction:

  • TF-Target Interactions: Maps internal transcriptional regulatory interaction specifically between the provided genes to identify key driver hubs.
  • Kinase-Substrate Interactions: Maps specific phosphorylation events and signaling cascades among the user input genes.
Both tools render dynamic, interactive graphs where users can select individual nodes to highlight specific interactions and local neighborhoods. For further analysis and reporting, these visualizations can be exported in high-quality HTML format, and the raw interaction data is summarized in an accompanying downloadable table.

DISCLAIMER

AgingHallmarksDB is an interactive web platform of genes associated with the hallmarks of aging that have been curated from established reference databases. The authors are not liable for any inaccuracies or omissions of any genes or annotations pertaining to aging-related associations in this resource. Users are advised to exercise their discretion while judging the weight of evidence for the gene-hallmark mapping and interactions compiled in this resource. Importantly, our sole goal in building this resource on the genetic landscape of aging is to enable future basic research on this topic, and it does not necessarily reflect the views or objectives of our employers or funders.