Rozowsky, Joel, Gao, Jiahao, Borsari, Beatrice, Yang, Yucheng T, Galeev, Timur, Gürsoy, Gamze, Epstein, Charles B, Xiong, Kun, Xu, Jinrui, Li, Tianxiao, Liu, Jason, Yu, Keyang, Berthel, Ana, Chen, Zhanlin, Navarro, Fabio, Sun, Maxwell S, Wright, James, Chang, Justin, Cameron, Christopher JF, Shoresh, Noam, Gaskell, Elizabeth, Drenkow, Jorg, Adrian, Jessika, Aganezov, Sergey, Aguet, François, Balderrama-Gutierrez, Gabriela, Banskota, Samridhi, Corona, Guillermo Barreto, Chee, Sora, Chhetri, Surya B, Cortez Martins, Gabriel Conte, Danyko, Cassidy, Davis, Carrie A, Farid, Daniel, Farrell, Nina P, Gabdank, Idan, Gofin, Yoel, Gorkin, David U, Gu, Mengting, Hecht, Vivian, Hitz, Benjamin C, Issner, Robbyn, Jiang, Yunzhe, Kirsche, Melanie, Kong, Xiangmeng, Lam, Bonita R, Li, Shantao, Li, Bian, Li, Xiqi, Lin, Khine Zin, Luo, Ruibang, Mackiewicz, Mark, Meng, Ran, Moore, Jill E, Mudge, Jonathan, Nelson, Nicholas, Nusbaum, Chad, Popov, Ioann, Pratt, Henry E, Qiu, Yunjiang, Ramakrishnan, Srividya, Raymond, Joe, Salichos, Leonidas, Scavelli, Alexandra, Schreiber, Jacob M, Sedlazeck, Fritz J, See, Lei Hoon, Sherman, Rachel M, Shi, Xu, Shi, Minyi, Sloan, Cricket Alicia, Strattan, J Seth, Tan, Zhen, Tanaka, Forrest Y, Vlasova, Anna, Wang, Jun, Werner, Jonathan, Williams, Brian, Xu, Min, Yan, Chengfei, Yu, Lu, Zaleski, Christopher, Zhang, Jing, Ardlie, Kristin, Cherry, J Michael, Mendenhall, Eric M, Noble, William S, Weng, Zhiping, Levine, Morgan E, Dobin, Alexander, Wold, Barbara, Mortazavi, Ali, Ren, Bing, Gillis, Jesse, Myers, Richard M, Snyder, Michael P, Choudhary, Jyoti, Milosavljevic, Aleksandar, Schatz, Michael C, Bernstein, Bradley E, Guigó, Roderic, Gingeras, Thomas R, Gerstein, Mark (March 2023) The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell, 186 (7). 1493-1511.e40. ISSN 0092-8674
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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.
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