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      A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

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          Abstract

          The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

          Abstract

          Super-enhancers and their associated transcription factor networks have been shown to influence ovarian cancer biology. Here, based on an integrated set of genomic and epigenomic datasets, the authors identify clinically relevant super-enhancers amplified in ovarian cancer patients and functionally validate their activity.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Trimmomatic: a flexible trimmer for Illumina sequence data

              Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                hfranco@med.unc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                22 July 2022
                22 July 2022
                2022
                : 13
                : 4247
                Affiliations
                [1 ]GRID grid.10698.36, ISNI 0000000122483208, Lineberger Comprehensive Cancer Center, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC 27599 USA
                [2 ]GRID grid.10698.36, ISNI 0000000122483208, Bioinformatics and Computational Biology Graduate Program, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC 27599 USA
                [3 ]GRID grid.10698.36, ISNI 0000000122483208, Thurston Arthritis Research Center, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC 27599 USA
                [4 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Cell Biology & Physiology, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC 27599 USA
                [5 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Genetics, School of Medicine, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC 27599 USA
                Author information
                http://orcid.org/0000-0003-4642-0118
                http://orcid.org/0000-0002-6388-7265
                http://orcid.org/0000-0001-9838-568X
                http://orcid.org/0000-0003-4051-3217
                http://orcid.org/0000-0003-2123-0051
                http://orcid.org/0000-0001-9354-0679
                Article
                31919
                10.1038/s41467-022-31919-8
                9307778
                35869079
                1388785e-6466-4475-9cb8-6fa81d241ab4
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 May 2021
                : 8 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: 5T32-CA217824
                Award ID: 5T32-CA217824
                Award ID: 5-P50-CA058223-25
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: T32-GM067553
                Award ID: R35-GM128645
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Funded by: FundRef https://doi.org/10.13039/100009634, Susan G. Komen (Susan G. Komen Breast Cancer Foundation);
                Award ID: CCR19608601
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100001368, V Foundation for Cancer Research (V Foundation);
                Award ID: V2019-015
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100002300, Rivkin Center for Ovarian Cancer (Rivkin Center);
                Award ID: N/A
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                gene regulatory networks,cancer genomics,transcriptomics,gene regulation,ovarian cancer

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