The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
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 m...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/56942 |
| Acesso em linha: | http://hdl.handle.net/10230/56942 http://dx.doi.org/10.1016/j.cell.2023.02.018 |
| Access Level: | acceso abierto |
| Palavra-chave: | Personal genome Allele-specific activity Functional epigenomes Predictive models eQTLs Genome annotations Transformer model Functional genomics ENCODE GTEx Structural variants Tissue specificity |
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The EN-TEx resource of multi-tissue personal epigenomes & variant-impact modelsRozowsky, JoelBorsari, Beatrice, 1992-Guigó Serra, RodericGerstein, Mark B.Personal genomeAllele-specific activityFunctional epigenomesPredictive modelseQTLsGenome annotationsTransformer modelFunctional genomicsENCODEGTExStructural variantsTissue specificityUnderstanding 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 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, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.Elsevier202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/56942http://dx.doi.org/10.1016/j.cell.2023.02.018reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésCell. 2023 Mar 30;186(7):1493-1511.e40© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/569422026-06-12T07:21:37Z |
| dc.title.none.fl_str_mv |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| title |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| spellingShingle |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models Rozowsky, Joel Personal genome Allele-specific activity Functional epigenomes Predictive models eQTLs Genome annotations Transformer model Functional genomics ENCODE GTEx Structural variants Tissue specificity |
| title_short |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| title_full |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| title_fullStr |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| title_full_unstemmed |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| title_sort |
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models |
| dc.creator.none.fl_str_mv |
Rozowsky, Joel Borsari, Beatrice, 1992- Guigó Serra, Roderic Gerstein, Mark B. |
| author |
Rozowsky, Joel |
| author_facet |
Rozowsky, Joel Borsari, Beatrice, 1992- Guigó Serra, Roderic Gerstein, Mark B. |
| author_role |
author |
| author2 |
Borsari, Beatrice, 1992- Guigó Serra, Roderic Gerstein, Mark B. |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Personal genome Allele-specific activity Functional epigenomes Predictive models eQTLs Genome annotations Transformer model Functional genomics ENCODE GTEx Structural variants Tissue specificity |
| topic |
Personal genome Allele-specific activity Functional epigenomes Predictive models eQTLs Genome annotations Transformer model Functional genomics ENCODE GTEx Structural variants Tissue specificity |
| 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 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, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023 2023 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10230/56942 http://dx.doi.org/10.1016/j.cell.2023.02.018 |
| url |
http://hdl.handle.net/10230/56942 http://dx.doi.org/10.1016/j.cell.2023.02.018 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Cell. 2023 Mar 30;186(7):1493-1511.e40 |
| dc.rights.none.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier |
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Elsevier |
| dc.source.none.fl_str_mv |
reponame:Repositorio Digital de la UPF instname:Universitat Pompeu Fabra |
| instname_str |
Universitat Pompeu Fabra |
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Repositorio Digital de la UPF |
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Repositorio Digital de la UPF |
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1869403365713117184 |
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15,811543 |