EchoGO: A Cross-Species Consensus Framework for Functional Enrichment in Non-Model Organisms

Zenodo (CERN European Organization for Nuclear Research)

Abstract

EchoGO v0.1.1 This release updates the README with comprehensive, cross-platform installation instructions for Linux, macOS, and Windows, including optional RRvGO and HTML-report dependencies. No code changes were made. EchoGO is an R toolkit for cross-species functional enrichment, designed for non-model organisms, environmental genomics, and ecotoxicology. It integrates GOseq, g:Profiler (with and without custom backgrounds), semantic similarity reduction (RRvGO), and GO-term network analysis into a single, reproducible workflow. The pipeline generates strict and exploratory consensus enrichment results, helping researchers identify interpretable and biologically robust functional signatures across species and tools. Key Features End-to-end enrichment workflow From DE results and gene-count matrices to GO/KEGG enrichment, consensus scoring, semantic reduction, and GO-term networks.Convenient wrappers include echogo_quickstart(), echogo_scaffold(), echogo_run(), and run_full_echogo(). Consensus enrichment across tools and species EchoGO unifies: GOseq (length-bias-corrected enrichment) g:Profiler (orthology-based enrichment; with and without background) Species-level support (species prevalence across orthologs) Depth-weighted fold enrichment and harmonized p-values It produces consensus scores, origin tags, and strict vs exploratory term sets. Built for non-model species Includes extensive helper functions for species selection: echogo_pick_species() echogo_preflight_species() echogo_resolve() for taxonomy- and tag-based species queries Interpretation and diagnostics EchoGO provides: EQI and fold-enrichment distributions Rarefaction and depth-saturation curves RRvGO semantic similarity reduction GO-term networks (strict and exploratory modes) These tools help users evaluate robustness, redundancy, and biological signal quality. Reproducible demo dataset A complete demonstration project is included, with curated example outputs matching the package vignettes. Installation Install EchoGO: remotes::install_github("miloes114/EchoGo", build_vignettes = TRUE) library(EchoGO) echogo_help() Install annotation databases (required): EchoGO::echogo_install_orgdb() or: EchoGO::echogo_install_orgdb(c("GO.db", "org.Mm.eg.db")) EchoGO requires GO.db and at least one OrgDb package. Optional dependencies: RRvGO semantic reduction: BiocManager::install("rrvgo") HTML report generation: install.packages(c( "rmarkdown", "knitr", "DT", "gt", "patchwork", "ggforce", "plotly", "visNetwork", "ggtext" )) Test the full workflow: echogo_quickstart(run_demo = TRUE) Documentation Full documentation and vignettes are distributed with the package: EchoGO Workflow (running the pipeline) EchoGO Interpretation (consensus, RRvGO, and networks) After installation: browseVignettes("EchoGO") Citation Escobar-Sierra, C., Langschied, F., & Inostroza, P. A. (2025).EchoGO: Cross-Species Consensus Enrichment for Non-Model Ecotoxicology Systems.Zenodo. https://doi.org/10.5281/zenodo.17658715 Changes in this version Expanded, cross-platform installation instructions Clarified optional dependencies (RRvGO, HTML report) Improved documentation and README usability No changes to the core EchoGO codebase Support Please report issues or feature requests on the GitHub issue tracker:https://github.com/miloes114/EchoGo/issues Contributions are welcome.

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