{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rozowsky J"],"funding":["NIMH NIH HHS","NHGRI NIH HHS","NCI NIH HHS","NLM NIH HHS"],"pagination":["1493-1511.e40"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10074325"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["186(7)"],"pubmed_abstract":["Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to v"],"journal":["Cell"],"pubmed_title":["The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models."],"pmcid":["PMC10074325"],"funding_grant_id":["R01 MH113005","U54 HG006991","UM1 HG009442","R01 LM012736","R01 MH101814","U54 HG007004","UM1 HG009390","U24 HG009446","U24 HG006620","P30 CA045508","R01 HG009318","U24 HG009397","U01 CA253481","U24 HG009649"],"pubmed_authors":["Shi M","Zhang J","Farid D","Schreiber JM","Shi X","Williams B","Hitz BC","Moore JE","Chhetri SB","Wright J","Ardlie K","Nusbaum C","Mendenhall EM","Kirsche M","Xu M","Salichos L","Noble WS","Rozowsky J","Strattan JS","Cameron CJF","Xu J","Danyko C","Cherry JM","Ramakrishnan S","Choudhary J","Sedlazeck FJ","Gerstein M","Yu L","Meng R","Mortazavi A","Adrian J","Schatz MC","Yu K","Balderrama-Gutierrez G","Wold B","Issner R","Borsari B","Li B","Chang J","Aguet F","Gursoy G","Sun MS","Davis CA","Scavelli A","Werner J","Myers RM","Ren B","Gofin Y","Gingeras TR","Yang YT","Farrell NP","Berthel A","Pratt HE","Kong X","Gorkin DU","Xiong K","Gillis J","Gao J","Corona GB","Wang J","Milosavljevic A","Qiu Y","Drenkow J","Chen Z","Hecht V","Zaleski C","Tanaka FY","Gu M","Weng Z","Sherman RM","Mackiewicz M","Galeev T","Levine ME","Bernstein BE","Nelson N","Li X","Li S","Li T","Dobin A","Tan Z","Vlasova A","Popov I","Mudge J","Lam BR","Liu J","Guigo R","Sloan CA","Jiang Y","Raymond J","Lin KZ","Gabdank I","See LH","Navarro F","Shoresh N","Snyder MP","Banskota S","Chee S","Cortez Martins GC","Epstein CB","Gaskell E","Aganezov S","Yan C","Luo R"],"additional_accession":[]},"is_claimable":false,"name":"The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.","description":"Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to v","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2026-05-28T21:51:57.562Z","creation":"2025-04-05T23:17:44.379Z"},"accession":"S-EPMC10074325","cross_references":{"pubmed":["37001506"],"doi":["10.1016/j.cell.2023.02.018"]}}