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

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

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...

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Bibliographic Details
Main Authors: Ekaterina D. Osintseva, German A. Ashniev, Alexey V. Orlov, Petr I. Nikitin, Zoia G. Zaitseva, Vladimir V. Volkov, Natalia N. Orlova
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Data
Subjects:
super-enhancers
non-binary classification
ChIP-seq
tumor biology
enhancer clustering
epigenetics
Online Access:https://www.mdpi.com/2306-5729/10/5/74
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