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

Joel Rozowsky; Jiahao Gao; Beatrice Borsari; Yucheng T. Yang; Timur Galeev; Gamze Gürsoy; Charles B. Epstein; Kun Xiong; Jinrui Xu; Tianxiao Li; Jason Liu; Keyang Yu; Ana Berthel; Zhanlin Chen; Fabio Navarro; Maxwell S. Sun; James Wright; Justin Chang; Christopher J.F. Cameron; Noam Shoresh; Elizabeth Gaskell; Jorg Drenkow; Jessika Adrian; Sergey Aganezov; François Aguet; Gabriela Balderrama-Gutierrez; Samridhi Banskota; Guillermo Barreto Corona; Sora Chee; Surya B. Chhetri; Gabriel Conte Cortez Martins; Cassidy Danyko; Carrie A. Davis; Daniel Farid; Nina P. Farrell; Idan Gabdank; Yoel Gofin; David U. Gorkin; Mengting Gu; Vivian Hecht; Benjamin C. Hitz; Robbyn Issner; Yunzhe Jiang; Melanie Kirsche; Xiangmeng Kong; Bonita R. Lam; Shantao Li; Bian Li; Xiqi Li; Khine Zin Lin; Ruibang Luo; Mark Mackiewicz; Ran Meng; Jill E. Moore; Jonathan Mudge; Nicholas Nelson; Chad Nusbaum; Ioann Popov; Henry E. Pratt; Yunjiang Qiu; Srividya Ramakrishnan; Joe Raymond; Leonidas Salichos; Alexandra Scavelli; Jacob M. Schreiber; Fritz J. Sedlazeck; Lei Hoon See; Rachel M. Sherman; Xu Shi; Minyi Shi; Cricket Alicia Sloan; J. Seth Strattan; Zhen Tan; Forrest Y. Tanaka; Anna Vlasova; Jun Wang; Jonathan Werner; Brian Williams; Min Xu; Chengfei Yan; Lu Yu; Christopher Zaleski; Jing Zhang; Kristin Ardlie; J. Michael Cherry; Eric M. Mendenhall; William S. Noble; Zhiping Weng; Morgan E. Levine; Alexander Dobin; Barbara Wold; Ali Mortazavi; Bing Ren; Jesse Gillis; Richard M. Myers; Michael P. Snyder; Jyoti Choudhary; Aleksandar Milosavljevic; Michael C. Schatz; Bradley E. Bernstein; Roderic Guigó; Thomas R. Gingeras; Mark Gerstein

doi:10.1016/j.cell.2023.02.018

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

Joel Rozowsky, Jiahao Gao, Beatrice Borsari, Yucheng T. Yang, Timur Galeev, Gamze Gürsoy, Charles B. Epstein, Kun Xiong, Jinrui Xu, Tianxiao Li, Jason Liu, Keyang Yu, Ana Berthel, Zhanlin Chen, Fabio Navarro, Maxwell S. Sun, James Wright, Justin Chang, Christopher J.F. Cameron, Noam ShoreshElizabeth Gaskell, Jorg Drenkow, Jessika Adrian, Sergey Aganezov, François Aguet, Gabriela Balderrama-Gutierrez, Samridhi Banskota, Guillermo Barreto Corona, Sora Chee, Surya B. Chhetri, Gabriel Conte Cortez Martins, Cassidy Danyko, Carrie A. Davis, Daniel Farid, Nina P. Farrell, Idan Gabdank, Yoel Gofin, David U. Gorkin, Mengting Gu, Vivian Hecht, Benjamin C. Hitz, Robbyn Issner, Yunzhe Jiang, Melanie Kirsche, Xiangmeng Kong, Bonita R. Lam, Shantao Li, Bian Li, Xiqi Li, Khine Zin Lin, Ruibang Luo, Mark Mackiewicz, Ran Meng, Jill E. Moore, Jonathan Mudge, Nicholas Nelson, Chad Nusbaum, Ioann Popov, Henry E. Pratt, Yunjiang Qiu, Srividya Ramakrishnan, Joe Raymond, Leonidas Salichos, Alexandra Scavelli, Jacob M. Schreiber, Fritz J. Sedlazeck, Lei Hoon See, Rachel M. Sherman, Xu Shi, Minyi Shi, Cricket Alicia Sloan, J. Seth Strattan, Zhen Tan, Forrest Y. Tanaka, Anna Vlasova, Jun Wang, Jonathan Werner, Brian Williams, Min Xu, Chengfei Yan, Lu Yu, Christopher Zaleski, Jing Zhang, Kristin Ardlie, J. Michael Cherry, Eric M. Mendenhall, William S. Noble, Zhiping Weng, Morgan E. Levine, Alexander Dobin, Barbara Wold, Ali Mortazavi, Bing Ren, Jesse Gillis, Richard M. Myers, Michael P. Snyder, Jyoti Choudhary, Aleksandar Milosavljevic, Michael C. Schatz, Bradley E. Bernstein, Roderic Guigó, Thomas R. Gingeras, Mark Gerstein

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

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 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.

Original language	English
Pages (from-to)	1493-1511.e40
Journal	Cell
Volume	186
Issue number	7
DOIs	https://doi.org/10.1016/j.cell.2023.02.018
State	Published - 30 Mar 2023
Externally published	Yes

Keywords

allele-specific activity
ENCODE
eQTLs
functional epigenomes
functional genomics
genome annotations
GTEx
personal genome
predictive models
structural variants
tissue specificity
transformer model

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

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10.1016/j.cell.2023.02.018

Cite this

Rozowsky, J., Gao, J., Borsari, B., Yang, Y. T., Galeev, T., Gürsoy, G., Epstein, C. B., Xiong, K., Xu, J., Li, T., Liu, J., Yu, K., Berthel, A., Chen, Z., Navarro, F., Sun, M. S., Wright, J., Chang, J., Cameron, C. J. F., ... Gerstein, M. (2023). The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell, 186(7), 1493-1511.e40. https://doi.org/10.1016/j.cell.2023.02.018

@article{2c2dd1490c2f42828f5db0f0da46896c,

title = "The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models",

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 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.",

keywords = "allele-specific activity, ENCODE, eQTLs, functional epigenomes, functional genomics, genome annotations, GTEx, personal genome, predictive models, structural variants, tissue specificity, transformer model",

author = "Joel Rozowsky and Jiahao Gao and Beatrice Borsari and Yang, {Yucheng T.} and Timur Galeev and Gamze G{\"u}rsoy and Epstein, {Charles B.} and Kun Xiong and Jinrui Xu and Tianxiao Li and Jason Liu and Keyang Yu and Ana Berthel and Zhanlin Chen and Fabio Navarro and Sun, {Maxwell S.} and James Wright and Justin Chang and Cameron, {Christopher J.F.} and Noam Shoresh and Elizabeth Gaskell and Jorg Drenkow and Jessika Adrian and Sergey Aganezov and Fran{\c c}ois Aguet and Gabriela Balderrama-Gutierrez and Samridhi Banskota and Corona, {Guillermo Barreto} and Sora Chee and Chhetri, {Surya B.} and {Cortez Martins}, {Gabriel Conte} and Cassidy Danyko and Davis, {Carrie A.} and Daniel Farid and Farrell, {Nina P.} and Idan Gabdank and Yoel Gofin and Gorkin, {David U.} and Mengting Gu and Vivian Hecht and Hitz, {Benjamin C.} and Robbyn Issner and Yunzhe Jiang and Melanie Kirsche and Xiangmeng Kong and Lam, {Bonita R.} and Shantao Li and Bian Li and Xiqi Li and Lin, {Khine Zin} and Ruibang Luo and Mark Mackiewicz and Ran Meng and Moore, {Jill E.} and Jonathan Mudge and Nicholas Nelson and Chad Nusbaum and Ioann Popov and Pratt, {Henry E.} and Yunjiang Qiu and Srividya Ramakrishnan and Joe Raymond and Leonidas Salichos and Alexandra Scavelli and Schreiber, {Jacob M.} and Sedlazeck, {Fritz J.} and See, {Lei Hoon} and Sherman, {Rachel M.} and Xu Shi and Minyi Shi and Sloan, {Cricket Alicia} and Strattan, {J. Seth} and Zhen Tan and Tanaka, {Forrest Y.} and Anna Vlasova and Jun Wang and Jonathan Werner and Brian Williams and Min Xu and Chengfei Yan and Lu Yu and Christopher Zaleski and Jing Zhang and Kristin Ardlie and Cherry, {J. Michael} and Mendenhall, {Eric M.} and Noble, {William S.} and Zhiping Weng and Levine, {Morgan E.} and Alexander Dobin and Barbara Wold and Ali Mortazavi and Bing Ren and Jesse Gillis and Myers, {Richard M.} and Snyder, {Michael P.} and Jyoti Choudhary and Aleksandar Milosavljevic and Schatz, {Michael C.} and Bernstein, {Bradley E.} and Roderic Guig{\'o} and Gingeras, {Thomas R.} and Mark Gerstein",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = mar,

day = "30",

doi = "10.1016/j.cell.2023.02.018",

language = "English",

volume = "186",

pages = "1493--1511.e40",

journal = "Cell",

issn = "0092-8674",

publisher = "Elsevier B.V.",

number = "7",

}

Rozowsky, J, Gao, J, Borsari, B, Yang, YT, Galeev, T, Gürsoy, G, Epstein, CB, Xiong, K, Xu, J, Li, T, Liu, J, Yu, K, Berthel, A, Chen, Z, Navarro, F, Sun, MS, Wright, J, Chang, J, Cameron, CJF, Shoresh, N, Gaskell, E, Drenkow, J, Adrian, J, Aganezov, S, Aguet, F, Balderrama-Gutierrez, G, Banskota, S, Corona, GB, Chee, S, Chhetri, SB, Cortez Martins, GC, Danyko, C, Davis, CA, Farid, D, Farrell, NP, Gabdank, I, Gofin, Y, Gorkin, DU, Gu, M, Hecht, V, Hitz, BC, Issner, R, Jiang, Y, Kirsche, M, Kong, X, Lam, BR, Li, S, Li, B, Li, X, Lin, KZ, Luo, R, Mackiewicz, M, Meng, R, Moore, JE, Mudge, J, Nelson, N, Nusbaum, C, Popov, I, Pratt, HE, Qiu, Y, Ramakrishnan, S, Raymond, J, Salichos, L, Scavelli, A, Schreiber, JM, Sedlazeck, FJ, See, LH, Sherman, RM, Shi, X, Shi, M, Sloan, CA, Strattan, JS, Tan, Z, Tanaka, FY, Vlasova, A, Wang, J, Werner, J, Williams, B, Xu, M, Yan, C, Yu, L, Zaleski, C, Zhang, J, Ardlie, K, Cherry, JM, Mendenhall, EM, Noble, WS, Weng, Z, Levine, ME, Dobin, A, Wold, B, Mortazavi, A, Ren, B, Gillis, J, Myers, RM, Snyder, MP, Choudhary, J, Milosavljevic, A, Schatz, MC, Bernstein, BE, Guigó, R, Gingeras, TR & Gerstein, M 2023, 'The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models', Cell, vol. 186, no. 7, pp. 1493-1511.e40. https://doi.org/10.1016/j.cell.2023.02.018

TY - JOUR

T1 - The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

AU - Rozowsky, Joel

AU - Gao, Jiahao

AU - Borsari, Beatrice

AU - Yang, Yucheng T.

AU - Galeev, Timur

AU - Gürsoy, Gamze

AU - Epstein, Charles B.

AU - Xiong, Kun

AU - Xu, Jinrui

AU - Li, Tianxiao

AU - Liu, Jason

AU - Yu, Keyang

AU - Berthel, Ana

AU - Chen, Zhanlin

AU - Navarro, Fabio

AU - Sun, Maxwell S.

AU - Wright, James

AU - Chang, Justin

AU - Cameron, Christopher J.F.

AU - Shoresh, Noam

AU - Gaskell, Elizabeth

AU - Drenkow, Jorg

AU - Adrian, Jessika

AU - Aganezov, Sergey

AU - Aguet, François

AU - Balderrama-Gutierrez, Gabriela

AU - Banskota, Samridhi

AU - Corona, Guillermo Barreto

AU - Chee, Sora

AU - Chhetri, Surya B.

AU - Cortez Martins, Gabriel Conte

AU - Danyko, Cassidy

AU - Davis, Carrie A.

AU - Farid, Daniel

AU - Farrell, Nina P.

AU - Gabdank, Idan

AU - Gofin, Yoel

AU - Gorkin, David U.

AU - Gu, Mengting

AU - Hecht, Vivian

AU - Hitz, Benjamin C.

AU - Issner, Robbyn

AU - Jiang, Yunzhe

AU - Kirsche, Melanie

AU - Kong, Xiangmeng

AU - Lam, Bonita R.

AU - Li, Shantao

AU - Li, Bian

AU - Li, Xiqi

AU - Lin, Khine Zin

AU - Luo, Ruibang

AU - Mackiewicz, Mark

AU - Meng, Ran

AU - Moore, Jill E.

AU - Mudge, Jonathan

AU - Nelson, Nicholas

AU - Nusbaum, Chad

AU - Popov, Ioann

AU - Pratt, Henry E.

AU - Qiu, Yunjiang

AU - Ramakrishnan, Srividya

AU - Raymond, Joe

AU - Salichos, Leonidas

AU - Scavelli, Alexandra

AU - Schreiber, Jacob M.

AU - Sedlazeck, Fritz J.

AU - See, Lei Hoon

AU - Sherman, Rachel M.

AU - Shi, Xu

AU - Shi, Minyi

AU - Sloan, Cricket Alicia

AU - Strattan, J. Seth

AU - Tan, Zhen

AU - Tanaka, Forrest Y.

AU - Vlasova, Anna

AU - Wang, Jun

AU - Werner, Jonathan

AU - Williams, Brian

AU - Xu, Min

AU - Yan, Chengfei

AU - Yu, Lu

AU - Zaleski, Christopher

AU - Zhang, Jing

AU - Ardlie, Kristin

AU - Cherry, J. Michael

AU - Mendenhall, Eric M.

AU - Noble, William S.

AU - Weng, Zhiping

AU - Levine, Morgan E.

AU - Dobin, Alexander

AU - Wold, Barbara

AU - Mortazavi, Ali

AU - Ren, Bing

AU - Gillis, Jesse

AU - Myers, Richard M.

AU - Snyder, Michael P.

AU - Choudhary, Jyoti

AU - Milosavljevic, Aleksandar

AU - Schatz, Michael C.

AU - Bernstein, Bradley E.

AU - Guigó, Roderic

AU - Gingeras, Thomas R.

AU - Gerstein, Mark

PY - 2023/3/30

Y1 - 2023/3/30

N2 - 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.

AB - 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.

KW - allele-specific activity

KW - ENCODE

KW - eQTLs

KW - functional epigenomes

KW - functional genomics

KW - genome annotations

KW - GTEx

KW - personal genome

KW - predictive models

KW - structural variants

KW - tissue specificity

KW - transformer model

UR - http://www.scopus.com/inward/record.url?scp=85150862475&partnerID=8YFLogxK

U2 - 10.1016/j.cell.2023.02.018

DO - 10.1016/j.cell.2023.02.018

M3 - Article

C2 - 37001506

AN - SCOPUS:85150862475

SN - 0092-8674

VL - 186

SP - 1493-1511.e40

JO - Cell

JF - Cell

IS - 7

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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