CenhANCER: a comprehensive cancer enhancer database for primary tissues and cell lines

G-quadruplexes as pivotal components of cis-regulatory elements in the human genome

BACKGROUND

Cis-regulatory elements (CREs) are crucial for regulating gene expression, and G-quadruplexes (G4s), as prototypal non-canonical DNA structures, may play a role in this regulation. However, the relationship between G4s and CREs, especially with non-promoter-like functional elements, requires further systematic investigation. We aimed to investigate the associations between G4s and human cCREs (candidate CREs) inferred from the Encyclopedia of DNA Elements (ENCODE) data.

RESULTS

We found that G4s are prominently enriched in most types of cCREs, especially those with promoter-like signatures (PLS). The co-occurrence of CTCF signals with H3K4me3 or H3K27ac signals strengthens the association between cCREs and G4s. Genetic variants in G4s, particularly within their G-runs, exhibit higher regulatory potential and deleterious effects compared to cCREs. The G-runs within G4s near transcriptional start sites (TSSs) are more evolutionarily constrained compared to G-runs in cCREs, while those far from the TSS are relatively less conserved. The presence of G4s is often linked to a more favorable local chromatin environment for the activation and execution of regulatory function of cCREs, potentially attributable to the formation of G4 secondary structures. Finally, we discovered that G4-associated cCREs exhibit widespread activation in a variety of cancers.

CONCLUSIONS

Our study suggests that G4s are integral components of human cis-regulatory elements, extending beyond their potential role in promoters. The G4 primary sequences are associated with the localization of CREs, while the G4 structures are linked to the activation of these elements. Therefore, we propose defining G4s as pivotal regulatory elements in the human genome.

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.