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

Abstract

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.

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multiomics Research – A Thorough Review on the Role of ChIP-Seq Data

“…On the other hand, the SE-TF regulatory network that plays a crucial role in the carcinogenesis of malignant tumours [57] has not been explored in connection to target gene prediction of miRNAs. Using ChIP-Seq data, a few databases identifying enhancers [50], and SEs [17] have become available in recent times, which might be explored to better understand the interplay between genes and their regulators. So, ChIP-Seq data has great potential to dissect the indirect regulatory role of miRNAs on the target genes.…”

“…Feature ROSE [6,14] Pipeline identifying SEs from ChIP-Seq data; separates SEs and typical enhancers HOMER [13] Software for motif discovery and ChIP-Seq analysis; identifies enhancers and SEs SEdb 2.0 [17] Database for SE resource and annotate the potential roles in gene transcription SEanalysis 2.0 [18] Web server for identifying association connecting SEs, pathways, TFs, and genes CenhANCER [19] Database for cancer enhancers from primary tissues and cell lines ENdb [20] Manually curated database of experimentally supported enhancers EnhancerAtlas 2.0 [21] Database with enhancer annotation across nine species TRmir [48] Database for miRNA related transcriptional regulation especially typical enhancer and SE EnhFFL [49] Database for enhancer related FFLs based on deterministic connections EnhancerDB [50] Database for enhancer related transcriptional regulatory associations…”

“…along with enhancer and SE identification. Based on ChIP-Seq experiments, SEs from cancer or normal tissue/cell lines are identified by databases such as SEdb 2.0 [17], SEanalysis [18], CenhANCER [19], ENdb [20], EnhancerAtlas 2.0 [21] etc.…”

Identification of a gefitinib resistance-associated signature for predicting prognosis and therapeutic response in lung adenocarcinoma via integrated multi-omics analysis and machine learning

“…The proliferation of public genome data has led to the rise of meta-analysis and computational modeling as pivotal methods for circumventing the constraints of insu cient statistical power in isolated studies (Wang et al 2023). In the past few years, regularized regression classi ers, such as the least absolute shrinkage and selection operator and elastic net, have become increasingly recognized as e cient tools for feature selection and prediction in high-dimensional datasets (Yang et al 2024;Fan et al 2024).…”