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Article: The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

TitleThe EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
Authors
Keywordsallele-specific activity
ENCODE
eQTLs
functional epigenomes
functional genomics
genome annotations
GTEx
personal genome
predictive models
structural variants
tissue specificity
transformer model
Issue Date30-Mar-2023
PublisherElsevier
Citation
Cell, 2023, v. 186, n. 7, p. 1493-+ How to Cite?
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 3 -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 variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, ENTEx provides rich data and generalizable models for more accurate personal functional genomics.


Persistent Identifierhttp://hdl.handle.net/10722/340461
ISSN
2023 Impact Factor: 45.5
2023 SCImago Journal Rankings: 24.342
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorRozowsky, J-
dc.contributor.authorGao, JH-
dc.contributor.authorBorsari, B-
dc.contributor.authorYang, YT-
dc.contributor.authorGaleev, T-
dc.contributor.authorGursoy, G-
dc.contributor.authorEpstein, CB-
dc.contributor.authorXiong, K-
dc.contributor.authorXu, JR-
dc.contributor.authorLi, TX-
dc.contributor.authorLiu, J-
dc.contributor.authorYu, KY-
dc.contributor.authorBerthel, A-
dc.contributor.authorChen, ZL-
dc.contributor.authorNavarro, F-
dc.contributor.authorSun, MS-
dc.contributor.authorWright, J-
dc.contributor.authorChang, JS-
dc.contributor.authorCameron, CJF-
dc.contributor.authorShoresh, N-
dc.contributor.authorGaskell, E-
dc.contributor.authorDrenkow, J-
dc.contributor.authorAdrian, J-
dc.contributor.authorAganezov, S-
dc.contributor.authorAguet, F-
dc.contributor.authorBalderrama-Gutierrez, G-
dc.contributor.authorBanskota, S-
dc.contributor.authorCorona, GB-
dc.contributor.authorChee, S-
dc.contributor.authorChhetri, SB-
dc.contributor.authorMartins, GCC-
dc.contributor.authorDanyko, C-
dc.contributor.authorDavis, CA-
dc.contributor.authorFarid, D-
dc.contributor.authorFarrell, NP-
dc.contributor.authorGabdank, I-
dc.contributor.authorGofin, Y-
dc.contributor.authorGorkin, DU-
dc.contributor.authorGu, MT-
dc.contributor.authorHecht, V-
dc.contributor.authorHitz, BC-
dc.contributor.authorIssner, R-
dc.contributor.authorJiang, YZ-
dc.contributor.authorKirsche, M-
dc.contributor.authorKong, XM-
dc.contributor.authorLam, BR-
dc.contributor.authorLi, ST-
dc.contributor.authorLi, B-
dc.contributor.authorLi, XQ-
dc.contributor.authorLin, KZ-
dc.contributor.authorLuo, RB-
dc.contributor.authorMackiewicz, M-
dc.contributor.authorMeng, R-
dc.contributor.authorMoore, JE-
dc.contributor.authorMudge, J-
dc.contributor.authorNelson, N-
dc.contributor.authorNusbaum, C-
dc.contributor.authorPopov, I-
dc.contributor.authorPratt, HE-
dc.contributor.authorQiu, YJ-
dc.contributor.authorRamakrishnan, S-
dc.contributor.authorRaymond, J-
dc.contributor.authorSalichos, L-
dc.contributor.authorScavelli, A-
dc.contributor.authorSchreiber, JM-
dc.contributor.authorSedlazeck, FJ-
dc.contributor.authorSee, LH-
dc.contributor.authorSherman, RM-
dc.contributor.authorShi, X-
dc.contributor.authorShi, MY-
dc.contributor.authorSloan, CA-
dc.contributor.authorStrattan, JS-
dc.contributor.authorTan, Z-
dc.contributor.authorTanaka, FY-
dc.contributor.authorVlasova, A-
dc.contributor.authorWang, J-
dc.contributor.authorWerner, J-
dc.contributor.authorWilliams, B-
dc.contributor.authorXu, M-
dc.contributor.authorYan, CF-
dc.contributor.authorYu, L-
dc.contributor.authorZaleski, C-
dc.contributor.authorZhang, J-
dc.contributor.authorArdlie, K-
dc.contributor.authorCherry, JM-
dc.contributor.authorMendenhall, EM-
dc.contributor.authorNoble, WS-
dc.contributor.authorWeng, ZP-
dc.contributor.authorLevine, ME-
dc.contributor.authorDobin, A-
dc.contributor.authorWold, B-
dc.contributor.authorMortazavi, A-
dc.contributor.authorRen, B-
dc.contributor.authorGillis, J-
dc.contributor.authorMyers, RM-
dc.contributor.authorSnyder, MP-
dc.contributor.authorChoudhary, J-
dc.contributor.authorMilosavljevic, A-
dc.contributor.authorSchatz, MC-
dc.contributor.authorBernstein, BE-
dc.contributor.authorGuigo, R-
dc.contributor.authorGingeras, TR-
dc.contributor.authorGerstein, M-
dc.date.accessioned2024-03-11T10:44:49Z-
dc.date.available2024-03-11T10:44:49Z-
dc.date.issued2023-03-30-
dc.identifier.citationCell, 2023, v. 186, n. 7, p. 1493-+-
dc.identifier.issn0092-8674-
dc.identifier.urihttp://hdl.handle.net/10722/340461-
dc.description.abstract<p>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 3 -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 variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, ENTEx provides rich data and generalizable models for more accurate personal functional genomics.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofCell-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectallele-specific activity-
dc.subjectENCODE-
dc.subjecteQTLs-
dc.subjectfunctional epigenomes-
dc.subjectfunctional genomics-
dc.subjectgenome annotations-
dc.subjectGTEx-
dc.subjectpersonal genome-
dc.subjectpredictive models-
dc.subjectstructural variants-
dc.subjecttissue specificity-
dc.subjecttransformer model-
dc.titleThe EN-TEx resource of multi-tissue personal epigenomes & variant-impact models-
dc.typeArticle-
dc.identifier.doi10.1016/j.cell.2023.02.018-
dc.identifier.pmid37001506-
dc.identifier.scopuseid_2-s2.0-85150862475-
dc.identifier.volume186-
dc.identifier.issue7-
dc.identifier.spage1493-
dc.identifier.epage+-
dc.identifier.eissn1097-4172-
dc.identifier.isiWOS:001037039300001-
dc.publisher.placeCAMBRIDGE-
dc.identifier.issnl0092-8674-

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