An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom anno...

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Published inNature communications Vol. 11; no. 1; pp. 3696 - 11
Main Authors Zhang, Jing, Lee, Donghoon, Dhiman, Vineet, Jiang, Peng, Xu, Jie, McGillivray, Patrick, Yang, Hongbo, Liu, Jason, Meyerson, William, Clarke, Declan, Gu, Mengting, Li, Shantao, Lou, Shaoke, Xu, Jinrui, Lochovsky, Lucas, Ung, Matthew, Ma, Lijia, Yu, Shan, Cao, Qin, Harmanci, Arif, Yan, Koon-Kiu, Sethi, Anurag, Gürsoy, Gamze, Schoenberg, Michael Rutenberg, Rozowsky, Joel, Warrell, Jonathan, Emani, Prashant, Yang, Yucheng T., Galeev, Timur, Kong, Xiangmeng, Liu, Shuang, Li, Xiaotong, Krishnan, Jayanth, Feng, Yanlin, Rivera-Mulia, Juan Carlos, Adrian, Jessica, Broach, James R, Bolt, Michael, Moran, Jennifer, Fitzgerald, Dominic, Dileep, Vishnu, Liu, Tingting, Mei, Shenglin, Sasaki, Takayo, Trevilla-Garcia, Claudia, Wang, Su, Wang, Yanli, Zang, Chongzhi, Wang, Daifeng, Klein, Robert J., Snyder, Michael, Gilbert, David M., Yip, Kevin, Cheng, Chao, Yue, Feng, Liu, X. Shirley, White, Kevin P., Gerstein, Mark
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 29.07.2020
Nature Publishing Group
Nature Portfolio
Subjects
45/15
45/23
45/47
45/91
49/15
49/47
631/114/2114
631/114/2401
631/208/69
Annotations
Cancer
Cell Line, Tumor
Cell Transformation, Neoplastic - genetics
CRISPR
Crosstalk
Databases, Genetic
Datasets
Encyclopedias
Gene Regulatory Networks
Genomes
Genomics
Hierarchies
Humanities and Social Sciences
Humans
multidisciplinary
Mutation - genetics
Myc protein
Neoplasms - genetics
Regulators
Reproducibility of Results
RNA-binding protein
Science
Science (multidisciplinary)
siRNA
Transcription factors
Transcription Factors - metabolism
Tumorigenesis
Tumors
Workflow
Online AccessGet full text

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Abstract ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource. ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.
AbstractList ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource. ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.
ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.
Abstract ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.
ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.
ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.
ArticleNumber 3696
Author Wang, Yanli
Xu, Jinrui
Fitzgerald, Dominic
Schoenberg, Michael Rutenberg
Zhang, Jing
Gilbert, David M.
Moran, Jennifer
Yu, Shan
Mei, Shenglin
Warrell, Jonathan
Li, Shantao
Trevilla-Garcia, Claudia
Feng, Yanlin
Cao, Qin
Li, Xiaotong
Rozowsky, Joel
Sasaki, Takayo
Snyder, Michael
Lou, Shaoke
Emani, Prashant
Wang, Su
Gerstein, Mark
Yan, Koon-Kiu
Adrian, Jessica
Dhiman, Vineet
Xu, Jie
Lee, Donghoon
Liu, Tingting
Yip, Kevin
Yue, Feng
Krishnan, Jayanth
Meyerson, William
Lochovsky, Lucas
Gürsoy, Gamze
Liu, X. Shirley
Sethi, Anurag
Klein, Robert J.
Zang, Chongzhi
White, Kevin P.
Kong, Xiangmeng
Clarke, Declan
Jiang, Peng
Liu, Jason
Galeev, Timur
Dileep, Vishnu
Wang, Daifeng
Ma, Lijia
Yang, Hongbo
Cheng, Chao
Yang, Yucheng T.
Ung, Matthew
McGillivray, Patrick
Bolt, Michael
Broach, James R
Rivera-Mulia, Juan Carlos
Harmanci, Arif
Liu, Shuang
Gu, Mengting
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32728046$$D View this record in MEDLINE/PubMed
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SSID ssj0000391844
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Snippet ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome...
Abstract ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for...
ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a...
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Annotations
Cancer
Cell Line, Tumor
Cell Transformation, Neoplastic - genetics
CRISPR
Crosstalk
Databases, Genetic
Datasets
Encyclopedias
Gene Regulatory Networks
Genomes
Genomics
Hierarchies
Humanities and Social Sciences
Humans
multidisciplinary
Mutation - genetics
Myc protein
Neoplasms - genetics
Regulators
Reproducibility of Results
RNA-binding protein
Science
Science (multidisciplinary)
siRNA
Transcription factors
Transcription Factors - metabolism
Tumorigenesis
Tumors
Workflow
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Title An integrative ENCODE resource for cancer genomics
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