Overview
The MetaOmixTools suite offers a unified web platform for meta-omics data integration and analysis. The top navigation bar includes four main entries and an About dropdown containing supplementary resources. Additionally, direct quick-access icons for Contact and GitHub are conveniently located in the top right corner of the navigation bar:
- Home
- MetaRank
- MetaEnrich
- User Guide
- About
- FAQ
- Gallery
- Contact
- GitHub
- Technical Specifications & Reproducibility
1 Home
The Home page serves as the main landing section for MetaOmixTools, providing:
General Overview: A concise panel introducing the purpose and scope of MetaOmixTools. It highlights the platform’s role in simplifying multi-study omics analysis and includes brief individual descriptions of each available tool.
Tool Access Buttons: This section features two buttons — one for MetaRank and another for MetaEnrich. Each tool is represented by a logo and name, which is an access button that links directly to its user interface.
Figure 1: MetaOmixTools Home Interface. Landing page displaying the general platform overview and direct access portals for the MetaRank and MetaEnrich modules.
- Footer: This section indicates the year of creation and the responsible entity, along with an image that redirects to the official website of that organisation.
Figure 2: MetaOmixTools Page Footer. Bottom section of the interface displaying the copyright year, the responsible entity, and a redirection link to the official website.
2 MetaRank
The MetaRank section loads the MetaRank tool, which enables consensus ranking of genes from multiple ordered gene lists derived from different studies or technologies.
- Input: Accepts gene lists through file upload (
.tsv,.csv,.txt) or direct paste into a text area. - Meta-analysis algorithms: Offers both weighted and unweighted ranking methods, including RankProd and RobustRankAggreg.
- Filters: Allows customization of gene inclusion criteria, such the minimum number of occurrences in lists and missing value management. Additionally, it enables the application of enrichment analysis to the full consensus gene list resulting from the meta-analysis.
- Output: Provides downloadable results including ranked tables in
.csvor.tsvformats and graphical summaries in.png,.jpgor.htmlformats.
For more information or detailed usage instructions, go to About → Instructions → MetaRank.
3 MetaEnrich
The MetaEnrich section gives access to functional enrichment analysis based on user-supplied lists derived from different studies or technologies.
- Input: Supports only
.txtformat, either uploaded or pasted. - Ontology options: Includes Gene Ontology terms or custom annotation inputs via
TERM2GENEandTERM2NAMEtables. - Parameters: Users can adjust the enrichment parameters, such as the organism, the minimum number of occurrences in lists or the database used, since in addition to GO, it is possible to use KEGG, Reactome or even custom annotations.
- Output: Provides downloadable results of enrichment tables in
.csvor.tsvformats and graphical summaries in.png,.jpgor.htmlformats.
For more information or detailed usage instructions, go to About → Instructions → MetaEnrich.
4 User Guide
Comprehensive documentation for each module of the suite, structured as follows:
- Overview: This introductory page, outlining the general navigation, layout, and primary components of the MetaOmixTools suite.
- MetaRank: A complete guide covering data preparation, algorithm selection, parameter configuration, and results exploration. It also includes best practices and troubleshooting tips.
- MetaRank Example Usage: Three step-by-step case studies demonstrating the full capabilities of the tool, ranging from a basic execution to an advanced configuration with varying input modalities.
- MetaEnrich: Detailed instructions for functional enrichment analysis, covering ontology selection, parameter tuning, and output interpretation, alongside best practices and error resolution.
- MetaEnrich Example Usage: Two practical case studies illustrating the different input methods and analytical workflows available within the module.
- Benchmarking: A comprehensive performance and methodological evaluation of the suite. This section details the rationale behind algorithmic selection, execution runtime scaling, stress testing under extreme data loads, and quantitative method concordance analyses (e.g., Jaccard similarity) to demonstrate the robustness and stability of the integrated pipelines.
5 About
The About dropdown provides essential support, troubleshooting resources, and supplementary materials to ensure a seamless user experience:
5.1 FAQ
A comprehensive repository of frequently asked questions designed to resolve common doubts and streamline your workflow. It is divided into two main categories:
- System and Data Preparation: Guidelines for properly formatting input gene lists, understanding dataset capacity, restoring analysis sessions via parameter loading, and details on exporting publication-ready tables and high-resolution, customizable graphics (PDF, SVG, 600 DPI raster images).
- Tool Usage and Technical Details: Clear explanations of the underlying statistical frameworks (e.g., RankProd, RRA, p-value combination methods), the methodological differences in enrichment between MetaRank and MetaEnrich, and the rationale behind specific data filtering steps like singleton exclusion.
5.2 Gallery
A curated visual showcase of typical outputs to illustrate the suite’s analytical and graphical capabilities:
- Interface Snapshots: Walkthrough screenshots demonstrating interactive features, parameter configuration options, and modular layouts.
- Consensus Ranking Visualizations: High-quality Heatmaps and distribution plots generated by the MetaRank module, alongside examples of final ranked tables and their associated top-gene enrichment Dot/Bar plots.
- Enrichment Results: Comprehensive Dot plots, Bar charts, and combined meta-analysis summary tables generated by MetaEnrich.
5.3 Contact
Direct channels to connect with the developers for inquiries, help, and feedback:
- Development Team: Official email addresses for reporting technical issues, requesting personalized support, or providing constructive suggestions to improve the suite.
5.4 GitHub
The central, open-source hub for collaboration, transparency, and version control:
- Repository: Direct link to the official MetaOmixTools GitHub page where you can access the full source code, track the latest updates, report issues via the issue tracker, and explore community contributions.
6 Technical Specifications & Reproducibility
MetaOmixTools is built with analytical reproducibility as a core design principle, in line with the FAIR data principles (Findable, Accessible, Interoperable, Reusable). The following measures and environmental controls are implemented throughout the suite.
6.1 1. Reproducibility Framework
To guarantee that analyses can be identically replicated across independent runs, the suite implements a rigid parameter tracking and export system.
Random Seed
All permutation-based analyses in MetaRank — including RankProd (RankProducts()) and RankProd Advanced (RP.advance()) — are executed with a fixed random seed (set.seed(123)) applied immediately before each call. This guarantees that p-values derived from permutation testing are numerically identical across independent runs on the same input data and software environment.
Session Parameter Export
Every analysis can be exported as a structured JSON parameters file via the Download parameters button available at the bottom of each results section. This file records: - All analysis parameters (database, organism, gene ID type, thresholds, colour settings, etc.) - Input data metadata: file names, number of rows, and SHA-256 checksums to verify data identity - Exact software and database versions at the time of export
Importing this file via the Load parameters button fully restores the configuration. Re-uploading the original input data and clicking Run Analysis reproduces the results exactly, provided the software environment has not changed.
Downloadable Analysis Report
The Configure & Download report button generates a self-contained HTML file containing the selected plots and results tables together with the complete parameter record and software version information. This file can be archived, shared with collaborators, or submitted as supplementary material alongside a publication.
6.2 2. Methodological Implementations
MetaOmixTools integrates widely established computational algorithms to ensure mathematical robustness: * Meta-analysis algorithms: Permutation-based rank product analysis are handled by the RankProd package. Robust rank aggregation (RRA) is executed via the RobustRankAggreg package. * Functional enrichment: Over-representation analysis for GO and KEGG is performed using clusterProfiler, Reactome analysis via ReactomePA, and disease ontology support is provided by DOSE. Live KEGG API interactions are managed through KEGGREST.
6.3 3. Computational Environment
The tables below list the exact versions of R and all key packages used in the current deployment of MetaOmixTools, generated on 2026-05-27 (server time zone: Europe/Madrid).
Runtime environment
| Component | Version |
|---|---|
| R | 4.6.0 (2026-04-24) |
| Platform | x86_64-pc-linux-gnu (Ubuntu 24.04.4 LTS) |
| Bioconductor | 3.23 |
Web framework
| Package | Version |
|---|---|
| shiny | 1.13.0 |
| bslib | 0.11.0 |
| shinyjs | 2.1.1 |
| shinyWidgets | 0.9.1 |
| shinycssloaders | 1.1.0 |
Meta-analysis algorithms
| Package | Version | Role |
|---|---|---|
| RankProd | 3.38.0 | Permutation-based rank product and rank sum meta-analysis |
| RobustRankAggreg | 1.2.1 | Robust rank aggregation (RRA) |
Functional enrichment
| Package | Version | Role |
|---|---|---|
| clusterProfiler | 4.20.0 | Over-representation analysis (GO, KEGG) |
| ReactomePA | 1.56.0 | Over-representation analysis (Reactome) |
| DOSE | 4.6.0 | Disease ontology support |
| KEGGREST | 1.52.0 | KEGG live API interface |
Annotation databases
| Database | Package version | Source | Update frequency |
|---|---|---|---|
| Gene Ontology (GO) | GO.db 3.23.1 | Bioconductor | ~1× per year (per Bioconductor release) |
| Human annotations | org.Hs.eg.db 3.23.1 | Bioconductor | ~1× per year |
| Mouse annotations | org.Mm.eg.db 3.23.0 | Bioconductor | ~1× per year |
| Rat annotations | org.Rn.eg.db 3.23.0 | Bioconductor | ~1× per year |
| Reactome | reactome.db 1.96.0 | Bioconductor | ~1× per year |
| KEGG | live API (accessed 2026-05-27) | kegg.jp | Continuous |
Note on database reproducibility. Gene–term mappings may differ between Bioconductor releases. The exact database version is automatically recorded in every exported parameters file (.json) and HTML report, allowing users to document the precise annotation context of any published analysis. KEGG is queried live at runtime; the access date is recorded in exported files accordingly.
Visualisation and data handling
| Package | Version |
|---|---|
| ggplot2 | 4.0.3 |
| plotly | 4.12.0 |
| UpSetR | 1.4.1 |
| RColorBrewer | 1.1-3 |
| viridis | 0.6.5 |
| dplyr | 1.2.1 |
| tidyr | 1.3.2 |
| data.table | 1.18.4 |
| DT | 0.34.0 |
| htmltools | 0.5.9 |
| htmlwidgets | 1.6.4 |
| jsonlite | 2.0.0 |
| digest | 0.6.39 |
| base64enc | 0.1-6 |
| markdown | 2.0 |