{
  "abstract": "Functional enrichment of transcriptomic datasets can be constrained by incomplete annotation, uneven identifier recognition, and uncertainty about which organism annotation resource provides the most informative functional context. EchoGO is a reproducible downstream framework that retains target GOseq enrichment as the primary functional evidence while evaluating the same transcriptomic response across researcher-selected organism annotation contexts using experiment-specific backgrounds. Evidence is assembled at the GO-term level with its analytical and organism provenance, allowing researchers to distinguish functions supported by the target analysis, functions also recovered through alternative annotation contexts, and additional context-derived hypotheses. Semantic reduction, gene-sharing networks, visualisation, and reporting organise this evidence into interpretable biological summaries. Validation across a reference-rich zebrafish dataset and two annotation-limited Gammarus pulex comparisons revealed contrasting patterns of annotation-context dependence. In zebrafish, 42 of 44 target GO terms were also recovered through alternative fish contexts, while additional contexts expanded the range of functional hypotheses. In G. pulex, limited identifier representation restricted contextual analysis; recovered functions were predominantly translation-related in one comparison and also included contractile biology in the other. EchoGO provides a traceable framework for examining how annotation availability and organism context shape functional interpretation while preserving the target analysis as the experimental anchor.",
  "abstract_shown": true,
  "affiliations": [
    "Chair of Ecotoxicology and Environmental Risk Assessment, Institute for Environmental Research, RWTH Aachen University, Aachen, Germany.",
    "Institute of Cell Biology and Neuroscience, Applied Bioinformatics Group, Goethe University Frankfurt, Frankfurt am Main, Germany.",
    "Department of Biological and Environmental Science, University of Gothenburg, Gothenburg, Sweden."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Camilo Escobar-Sierra"
    },
    {
      "affs": [
        1
      ],
      "name": "Felix Langschied"
    },
    {
      "affs": [
        0
      ],
      "name": "Angelina Miller"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Pedro A Inostroza"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0c9bn8ct8jfq6.bib",
  "cited_by": 0,
  "doi": "10.1016/j.mex.2026.104222",
  "fulltext_url": "https://europepmc.org/articles/PMC13742505",
  "id": "0c9bn8ct8jfq6",
  "issns": [
    "2215-0161"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997842",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7223171247",
  "pdf_url": "https://europepmc.org/articles/PMC13742505?pdf=render",
  "pmcid": "PMC13742505",
  "pmid": "42997842",
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0c9bn8ct8jfq6.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:41Z",
      "native_id": "MED:42997842",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997842"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Bioinformatics and Genomic Networks",
      "subfield": "molecular-biology"
    }
  ],
  "title": "EchoGO: annotation-context-aware functional interpretation of transcriptomic data.",
  "title_text": "EchoGO: annotation-context-aware functional interpretation of transcriptomic data.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:41Z",
  "url": "https://research.draconitic.com/paper/0c9bn8ct8jfq6",
  "venue": "MethodsX",
  "versions": []
}