Super-enhancers (SEs) are cis-regulatory elements of the human genome that have been widely discussed since the discovery and origin of the term. Super-enhancers have been shown to be strongly associated with the expression of genes crucial for cell differentiation, cell stability maintenance, and tumorigenesis. Our goal was to systematize research studies dedicated to the investigation of structure and functions of super-enhancers as well as to define further perspectives of the field in various applications, such as drug development and clinical use. We overviewed the fundamental studies which provided experimental data on various pathologies and their associations with particular super-enhancers. The analysis of mainstream approaches for SE search and prediction allowed us to accumulate existing data and propose directions for further algorithmic improvements of SEs’ reliability levels and efficiency. Thus, here we provide the description of the most robust algorithms such as ROSE, imPROSE, and DEEPSEN and suggest their further use for various research and development tasks. The most promising research direction, which is based on topic and number of published studies, are cancer-associated super-enhancers and prospective SE-targeted therapy strategies, most of which are discussed in this review.

The study discloses the application of transformer-based deep learning models for the task of super-enhancers prediction in human tumor cell lines with a specific focus on sequence-only features within studied entities of super-enhancer and enhancer elements in the human genome. The proposed SE-prediction method included the GENA-LM application at handling long DNA sequences with the classification task, distinguishing super-enhancers from enhancers using H3K36me, H3K4me1, H3K4me3 and H3K27ac landscape datasets from HeLa, HEK293, H2171, Jurkat, K562, MM1S and U87 cell lines. The model was fine-tuned on relevant sequence data, allowing for the analysis of extended genomic sequences without the need for epigenetic markers as proposed in early approaches. The study achieved balanced accuracy metrics, surpassing previous models like SENet, particularly in HEK293 and K562 cell lines. Also, it was shown that super-enhancers frequently co-localize with epigenetic marks such as H3K4me3 and H3K27ac. Therefore, the attention mechanism of the model provided insights into the sequence features contributing to SE classification, indicating a correlation between sequence-only features and mentioned epigenetic landscapes. These findings support the potential transformer models use in further genomic sequence analysis for bioinformatics applications in enhancer/super-enhancer characterization and gene regulation studies.

Super-enhancers (SEs) orchestrate high-level transcription by integrating multiple regulatory elements and signals. Although chromatin accessibility and transcription factor binding within SEs are extensively studied, the role of non-canonical DNA structures, particularly Z-DNA, remains underexplored. In this study, genome-wide predictions of Z-DNA-forming sequences (generated by the Z-DNA-BERT model) were applied to systematically investigate their distribution within typical enhancers and SEs across multiple human cancer cell lines. Statistically significant enrichment of Z-DNA sequences within SE regions, compared to random genomic controls, was observed. Furthermore, genetic variants overlapping these Z-DNA regions, identified using data from the 1000 Genomes Project, were found to alter binding motifs of the SP/KLF transcription factor family. These mutations exhibited population-specific clustering and overlapped previously reported pathogenic copy-number variations (CNVs) associated with neurodevelopmental disorders, potentially affecting transcription factor binding motifs related to neuronal growth and differentiation pathways. Population-level phylogenetic analysis revealed distinct clustering patterns of these variants, suggesting frequency-specific genetic architecture. Overall, the computational findings indicate that Z-DNA structures within super-enhancers might play regulatory roles and potentially influence population-specific genetic variation, highlighting specific genomic targets and providing new avenues for future experimental research.

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.

Long non-coding RNAs (lncRNAs) interact with chromatin and recruit epigenetic complexes to specific genomic loci, yet their relationship with super-enhancers (SEs), key regulatory elements frequently reprogrammed in cancer, remains unexplored. We developed an integrative pipeline that combines RNA–chromatin contact data (RNA-Chrom), histone modification–lncRNA expression correlation profiles (HiMoRNA peaks), and super-enhancer annotations (SEdb 3.0) to map lncRNA–SE regulatory axes. Applying this framework to SNHG1 in HCT116 colorectal cancer cells, we identified 21 SNHG1-reactive super-enhancers (Ψ-SEs) among 184 cancer-specific SEs, at which SNHG1 physical contacts co-occur with SNHG1-correlated histone modifications (HiMoRNA peaks), predominantly H3K4me1 (permutation p = 0.001, fold enrichment = 2.03). Comparison with 4145 lncRNAs demonstrated that epigenetic correlations alone do not distinguish SNHG1; instead, the addition of the contact layer is required to delineate the Ψ-SE set. Differential expression (DESeq2) and co-expression analyses in 471 TCGA-COAD tumor samples identified 12 Ψ-SE target genes (including CDC20, PDP1, and TOP1) consistently upregulated in both HCT116 cells and patient tumors and positively correlated with SNHG1, with the co-expression signal robust to tumor purity correction. The proposed Ψ/Ω classification provides a generalizable framework for prioritizing super-enhancers at which lncRNA–chromatin interactions may shape the local epigenetic environment across cancer types.