{"doi":"10.1007/s00335-025-10104-4","title":"IMPC impact on preclinical mouse models","abstract":"21 academic research institutions across 15 countries on 5 continents, including leading laboratories from Europe, North America, Asia, and Africa.IMPC members agreed a mission to \"create a comprehensive catalog of mammalian gene function that is freely available for researchers\" by producing mouse models with targeted disruptions of every human orthologous protein-coding gene in the mouse genome.These knockout models have been and continue to be subjected to a standardized series of phenotyping assays across multiple body systems (Brown and Moore 2012a, b;Brown et al. 2005), allowing for identification of key biological processes and functional pleiotropy (Brown and Lad 2019), sexual dimorphism (Karp et al. 2017;Wilson et al. 2022), and essentiality for each gene (Cacheiro et al. 2020).Depositing mice and data into publicly accessible repositories are making these resources available for researchers around the world to extend this new knowledge into studies of the genetic effects on specific disease mechanisms.These efforts aim to accelerate disease diagnoses, identify new druggable targets, develop novel therapeutic interventions, and enact effective disease prevention strategies (Groza et al. 2023). The impactTo date, data emerging from the study of IMPC mice has become an invaluable scientific resource for the biomedical research community, facilitating the study of gene function and the identification of novel therapeutic targets for human diseases.The vast phenotypic data generated not only in the project consortium itself but also by the greater biomedical research community using IMPC-generated mouse models and data has substantially enhanced our understanding of gene-disease relationships and genetic influences on mechanisms of disease.A publication tracking system using natural language processing methods, followed by annotator reviews through an IMPC-specific literature monitoring and curation tool (Cacheiro et al. 2024), identified nearly 7,500 papers that have used IMPC mice, data, and/or biomaterials The challengeComplete sequencing of genomes for human, mouse, and several other species was a technological breakthrough that identified and mapped thousands of genes and non-coding regions, much of which had heretofore been unknown.But it soon became apparent that significant knowledge gaps existed in understanding the in vivo function of most of these genes.Scientific research progress to address this deficiency was painstakingly slow and arduous, resulting in only partial functional annotation of a small number of well-characterized genes and gene sets.This self-fulfilling research paradigm overlooked genes with little to no known function, leaving in its wake a neglected \"dark\" genome.To accelerate progress and reveal gene function and insights into genetic associations and causes of disease, a fundamental shift from incremental steps to transformative change was needed.In response, a collaborative, global initiative emerged to systematically generate and phenotype a comprehensive collection of genetically modified \"knockout\" mouse models.","journal":"Mammalian Genome","year":2025,"id":537859,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":38.748356153786645,"corpus_rank":7799,"citation_count":4,"citer_count":2,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8195,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":2.0833,"fair_percentile":1.4062977682665851,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":266969,"name":"Pilar Cacheiro","orcid":"0000-0002-6335-8208","position":1,"is_corresponding":false},{"id":11700,"name":"Damian Smedley","orcid":"0000-0002-5836-9850","position":2,"is_corresponding":false},{"id":30197,"name":"K. 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NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"Data availability No datasets were generated or analysed during the current study.","why":"No data were produced, so no temporal commitment is stated.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For genomics / sequencing data, deposit in GEO (GSE accession), SRA (SRP/SRR) or ENA/BioProject (PRJEB/PRJNA).","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For genomics / sequencing data, deposit in GEO (GSE accession), SRA (SRP/SRR) or ENA/BioProject (PRJEB/PRJNA).","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For genomics / sequencing data, deposit in GEO (GSE accession), SRA (SRP/SRR) or ENA/BioProject (PRJEB/PRJNA).","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the genomics / sequencing repository accession (e.g. from GEO (GSE accession), SRA (SRP/SRR) or ENA/BioProject (PRJEB/PRJNA)) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T13:10:04.556163Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}