Back

Shiny AMMOA: an interactive platform for integrative multi-omics analysis of murine aging

Ninomiya Kanda, M.

2026-05-20 bioinformatics
10.64898/2026.05.18.726091 bioRxiv
Show abstract

Aging is accompanied by complex, tissue-specific molecular changes across multiple biological layers, yet integrative analysis of multi-omics datasets remains challenging for many experimental researchers due to technical and computational barriers. Here, I present Shiny Aging Murine Multi-Omic Analyzer (Shiny AMMOA), a graphical user interface (GUI)-based, user-friendly analytical platform that enables interactive exploration of murine aging-associated bulk transcriptomic, proteomic, and metabolomic datasets. Shiny AMMOA integrates publicly available multi-omics resources within a unified R Shiny framework and supports end-to-end analyses, including differential expression testing, pathway enrichment analysis, and pathway-level visualization across individual and multiple omics layers. Using representative use cases, I demonstrate that Shiny AMMOA recapitulates key findings from original source studies and facilitates intuitive discovery of tissue-, pathway-, and modality-specific aging signatures, including age-associated alterations in unfolded protein response, extracellular matrix organization, and metabolic pathways across specific tissues and omics layers. The platform further enables integrated visualization of molecular changes across omics layers on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams, supporting hypothesis generation at the systems level. By democratizing access to integrative multi-omics analysis while preserving analytical rigor, Shiny AMMOA provides an extensible resource for experimental biologists and aging researchers to interrogate large-scale public datasets, prioritize biological pathways, and accelerate translation of multi-omics insights into testable experimental hypotheses. Shiny AMMOA is available at https://github.com/M-Ninomiya-Kanda/Shiny_AMMOA_local, and a lightweight web-based demonstration version with limited functionality is available at https://m-ninomiya-kanda.shinyapps.io/shiny_ammoa_web/.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
Advanced Science
249 papers in training set
Top 1.0%
10.7%
2
Aging Cell
144 papers in training set
Top 0.9%
7.0%
3
Nature Communications
4913 papers in training set
Top 28%
6.5%
4
Briefings in Bioinformatics
326 papers in training set
Top 1%
5.0%
5
Nucleic Acids Research
1128 papers in training set
Top 4%
4.5%
6
Cell Systems
167 papers in training set
Top 4%
3.7%
7
Bioinformatics Advances
184 papers in training set
Top 1%
3.7%
8
Bioinformatics
1061 papers in training set
Top 5%
3.7%
9
eLife
5422 papers in training set
Top 24%
3.7%
10
Nature Aging
51 papers in training set
Top 0.6%
3.2%
50% of probability mass above
11
Computational and Structural Biotechnology Journal
216 papers in training set
Top 2%
3.0%
12
Patterns
70 papers in training set
Top 0.5%
2.2%
13
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 3%
1.9%
14
Aging
69 papers in training set
Top 1%
1.9%
15
PLOS Computational Biology
1633 papers in training set
Top 14%
1.9%
16
npj Aging
15 papers in training set
Top 0.4%
1.9%
17
GeroScience
97 papers in training set
Top 0.8%
1.9%
18
Cell Reports
1338 papers in training set
Top 23%
1.8%
19
Alzheimer's & Dementia
143 papers in training set
Top 2%
1.7%
20
Molecular & Cellular Proteomics
158 papers in training set
Top 1%
1.4%
21
Nature Medicine
117 papers in training set
Top 3%
1.4%
22
PLOS ONE
4510 papers in training set
Top 57%
1.4%
23
Cell Reports Methods
141 papers in training set
Top 4%
1.0%
24
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 40%
1.0%
25
iScience
1063 papers in training set
Top 24%
1.0%
26
Genome Medicine
154 papers in training set
Top 7%
0.9%
27
NAR Genomics and Bioinformatics
214 papers in training set
Top 3%
0.9%
28
GigaScience
172 papers in training set
Top 2%
0.9%
29
Scientific Reports
3102 papers in training set
Top 72%
0.8%
30
eneuro
389 papers in training set
Top 9%
0.8%