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A Non-Binary Approach to Super-Enhancer Identification and Clustering: A Dataset for Tumor- and Treatment-Associated Dynamics in Mouse Tissues

Author

Listed:
  • Ekaterina D. Osintseva

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia)

  • German A. Ashniev

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
    Faculty of Biology, Lomonosov Moscow State University, Leninskiye Gory, MSU, 1-12, 119991 Moscow, Russia
    Institute for Information Transmission Problems RAS, 127051 Moscow, Russia)

  • Alexey V. Orlov

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia)

  • Petr I. Nikitin

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
    National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoe Shosse, 115409 Moscow, Russia)

  • Zoia G. Zaitseva

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia)

  • Vladimir V. Volkov

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
    National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoe Shosse, 115409 Moscow, Russia)

  • Natalia N. Orlova

    (Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia)

Abstract

Super-enhancers (SEs) are large clusters of highly active enhancers that play key regulatory roles in cell identity, development, and disease. While conventional methods classify SEs in a binary fashion—super-enhancer or not—this threshold-based approach can overlook significant intermediate states of enhancer activity. Here, we present a dataset and accompanying framework that facilitate a more nuanced, non-binary examination of SE activation across mouse tissue types (mammary gland, lung tissue, and NMuMG cells) and various experimental conditions (normal, tumor, and drug-treated samples). By consolidating overlapping SE intervals and capturing continuous enhancer activity metrics (e.g., ChIP-seq signal intensities), our dataset reveals gradual transitions between moderate and high enhancer activity levels that are not captured by strictly binary classification. Additionally, the data include extensive functional annotations, linking SE loci to nearby genes and enabling immediate downstream analyses such as clustering and gene ontology enrichment. The flexible approach supports broader investigations of enhancer landscapes, offering a comprehensive platform for understanding how SE activation underpins disease mechanisms, therapeutic response, and developmental processes.

Suggested Citation

  • Ekaterina D. Osintseva & German A. Ashniev & Alexey V. Orlov & Petr I. Nikitin & Zoia G. Zaitseva & Vladimir V. Volkov & Natalia N. Orlova, 2025. "A Non-Binary Approach to Super-Enhancer Identification and Clustering: A Dataset for Tumor- and Treatment-Associated Dynamics in Mouse Tissues," Data, MDPI, vol. 10(5), pages 1-13, May.
  • Handle: RePEc:gam:jdataj:v:10:y:2025:i:5:p:74-:d:1655186
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