Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder

Recent advances in chromatin conformation capture technologies, such as SPRITE and Pore-C, have enabled the detection of simultaneous contacts among multiple chromatin loci. This has made it possible to investigate the cooperative transcriptional regulation involving multiple genes and regulatory elements at the resolution of a single molecule. However, these technologies are unavoidably subject to the random polymer looping effect and technical biases, making it challenging to distinguish genuine regulatory relationships directly from random polymer interactions. Here, we present HyperloopFinder, a method for identifying regulatory multi-way chromatin contacts (hyperloops) by jointly modeling the random polymer looping effect and technical biases to estimate the statistical significance of multi-way contacts. The results show that our model can accurately estimate the expected interaction frequency of multi-way contacts based on the distance distribution of pairwise contacts, revealing that most multi-way contacts can be formed by randomly linking the pairwise contacts adjacent to each other. Moreover, we observed the spatial colocalization of the interaction sites of hyperloops from image-based data. Our results also revealed that hyperloops can function as scaffolds for the cooperation among multiple genes and regulatory elements. In summary, our work contributes novel insights into higher-order chromatin structures and functions and has the potential to enhance our understanding of transcriptional regulation and other cellular processes.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Targeting the chromatin structural changes of antitumor immunity

Epigenomic imbalance drives abnormal transcriptional processes, promoting the onset and progression of cancer. Although defective gene regulation generally affects carcinogenesis and tumor suppression networks, tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes, which may have significant implications for the development and application of epigenetic therapy, cancer immunotherapy, and their combinations. Herein, we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes, DNA methylation, histone post-translational modification, and chromatin structure in tumor immunogenicity, and introduce these epigenetic research methods. We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immunotherapy through the complex interaction between cancer epigenetics and cancer immunology.

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.

In vitro characterization of alternative l-threonate and d-erythronate catabolic pathways

l-threonate is the metabolite of vitamin C, while d-erythronate is the metabolite of N-acetyl-d-glucosamine, the nutritional supplement for joint health. They are widely distributed in the environment and human biofluids. Nevertheless, the catabolisms of l-threonate and d-erythronate are sparsely reported. Here we explored the functional diversity of an acid sugar kinase family (Pfam families PF07005-PF17042), and discovered a novel 2-oxo-tetronate kinase. The conserved genome neighborhood of the 2-oxo-tetronate kinase encodes members of class-II fructose-bisphosphate aldolase family (F_bP_aldolase, PF01116) and a dehydrogenase family (PF03446-PF14833). Instructed by this analysis, we experimentally verified that these enzymes are capable of degrading l-threonate into dihydroxyacetone phosphate (DHAP) in Arthrobacter sp. ZBG10, Clostridium scindens ATCC 35704, and Pseudonocardia dioxanivorans ATCC 55486. Meanwhile, a convergent catabolic pathway for d-erythronate was characterized in P. dioxanivorans ATCC 55486. Moreover, the phylogenetic distribution analysis indicates that the biological range of the identified l-threonate and d-erythronate catabolic pathways appears to extend mostly to members of the Actinomycetota, Cyanobacteriota, Bacillota, Pseudomonadota, and Bacteroidota phyla.

The dynamic role of cohesin in maintaining human genome architecture

Recent advances in genomic and imaging techniques have revealed the complex manner of organizing billions of base pairs of DNA necessary for maintaining their functionality and ensuring the proper expression of genetic information. The SMC proteins and cohesin complex primarily contribute to forming higher-order chromatin structures, such as chromosomal territories, compartments, topologically associating domains (TADs) and chromatin loops anchored by CCCTC-binding factor (CTCF) protein or other genome organizers. Cohesin plays a fundamental role in chromatin organization, gene expression and regulation. This review aims to describe the current understanding of the dynamic nature of the cohesin-DNA complex and its dependence on cohesin for genome maintenance. We discuss the current 3C technique and numerous bioinformatics pipelines used to comprehend structural genomics and epigenetics focusing on the analysis of Cohesin-centred interactions. We also incorporate our present comprehension of Loop Extrusion (LE) and insights from stochastic modelling.

3D organization of regulatory elements for transcriptional regulation in Arabidopsis

BACKGROUND

Although spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants.

RESULTS

Here we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression.

CONCLUSION

The results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.

Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes

Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

Applying multi-omics toward tumor microbiome research

Rather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies. However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.

ChromLoops: a comprehensive database for specific protein-mediated chromatin loops in diverse organisms

Chromatin loops (or chromatin interactions) are important elements of chromatin structures. Disruption of chromatin loops is associated with many diseases, such as cancer and polydactyly. A few methods, including ChIA-PET, HiChIP and PLAC-Seq, have been proposed to detect high-resolution, specific protein-mediated chromatin loops. With rapid progress in 3D genomic research, ChIA-PET, HiChIP and PLAC-Seq datasets continue to accumulate, and effective collection and processing for these datasets are urgently needed. Here, we developed a comprehensive, multispecies and specific protein-mediated chromatin loop database (ChromLoops, https://3dgenomics.hzau.edu.cn/chromloops), which integrated 1030 ChIA-PET, HiChIP and PLAC-Seq datasets from 13 species, and documented 1 491 416 813 high-quality chromatin loops. We annotated genes and regions overlapping with chromatin loop anchors with rich functional annotations, such as regulatory elements (enhancers, super-enhancers and silencers), variations (common SNPs, somatic SNPs and eQTLs), and transcription factor binding sites. Moreover, we identified genes with high-frequency chromatin interactions in the collected species. In particular, we identified genes with high-frequency interactions in cancer samples. We hope that ChromLoops will provide a new platform for studying chromatin interaction regulation in relation to biological processes and disease.

Rewiring of 3D Chromatin Topology Orchestrates Transcriptional Reprogramming and the Development of Human Dilated Cardiomyopathy

BACKGROUND

Transcriptional reconfiguration is central to heart failure, the most common cause of which is dilated cardiomyopathy (DCM). The effect of 3-dimensional chromatin topology on transcriptional dysregulation and pathogenesis in human DCM remains elusive.

METHODS

We generated a compendium of 3-dimensional epigenome and transcriptome maps from 101 biobanked human DCM and nonfailing heart tissues through highly integrative chromatin immunoprecipitation (H3K27ac [acetylation of lysine 27 on histone H3]), in situ high-throughput chromosome conformation capture, chromatin immunoprecipitation sequencing, assay for transposase-accessible chromatin using sequencing, and RNA sequencing. We used human induced pluripotent stem cell-derived cardiomyocytes and mouse models to interrogate the key transcription factor implicated in 3-dimensional chromatin organization and transcriptional regulation in DCM pathogenesis.

RESULTS

We discovered that the active regulatory elements (H3K27ac peaks) and their connectome (H3K27ac loops) were extensively reprogrammed in DCM hearts and contributed to transcriptional dysregulation implicated in DCM development. For example, we identified that nontranscribing NPPA-AS1 (natriuretic peptide A antisense RNA 1) promoter functions as an enhancer and physically interacts with the NPPA (natriuretic peptide A) and NPPB (natriuretic peptide B) promoters, leading to the cotranscription of NPPA and NPPB in DCM hearts. We revealed that DCM-enriched H3K27ac loops largely resided in conserved high-order chromatin architectures (compartments, topologically associating domains) and their anchors unexpectedly had equivalent chromatin accessibility. We discovered that the DCM-enriched H3K27ac loop anchors exhibited a strong enrichment for HAND1 (heart and neural crest derivatives expressed 1), a key transcription factor involved in early cardiogenesis. In line with this, its protein expression was upregulated in human DCM and mouse failing hearts. To further validate whether HAND1 is a causal driver for the reprogramming of enhancer-promoter connectome in DCM hearts, we performed comprehensive 3-dimensional epigenome mappings in human induced pluripotent stem cell-derived cardiomyocytes. We found that forced overexpression of HAND1 in human induced pluripotent stem cell-derived cardiomyocytes induced a distinct gain of enhancer-promoter connectivity and correspondingly increased the expression of their connected genes implicated in DCM pathogenesis, thus recapitulating the transcriptional signature in human DCM hearts. Electrophysiology analysis demonstrated that forced overexpression of HAND1 in human induced pluripotent stem cell-derived cardiomyocytes induced abnormal calcium handling. Furthermore, cardiomyocyte-specific overexpression of Hand1 in the mouse hearts resulted in dilated cardiac remodeling with impaired contractility/Ca2+ handling in cardiomyocytes, increased ratio of heart weight/body weight, and compromised cardiac function, which were ascribed to recapitulation of transcriptional reprogramming in DCM.

CONCLUSIONS

This study provided novel chromatin topology insights into DCM pathogenesis and illustrated a model whereby a single transcription factor (HAND1) reprograms the genome-wide enhancer-promoter connectome to drive DCM pathogenesis.

3D chromatin architecture and transcription regulation in cancer

Chromatin has distinct three-dimensional (3D) architectures important in key biological processes, such as cell cycle, replication, differentiation, and transcription regulation. In turn, aberrant 3D structures play a vital role in developing abnormalities and diseases such as cancer. This review discusses key 3D chromatin structures (topologically associating domain, lamina-associated domain, and enhancer-promoter interactions) and corresponding structural protein elements mediating 3D chromatin interactions [CCCTC-binding factor, polycomb group protein, cohesin, and Brother of the Regulator of Imprinted Sites (BORIS) protein] with a highlight of their associations with cancer. We also summarise the recent development of technologies and bioinformatics approaches to study the 3D chromatin interactions in gene expression regulation, including crosslinking and proximity ligation methods in the bulk cell population (ChIA-PET and HiChIP) or single-molecule resolution (ChIA-drop), and methods other than proximity ligation, such as GAM, SPRITE, and super-resolution microscopy techniques.

Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies

Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

In situ Chromatin Interaction Analysis Using Paired-End Tag Sequencing

Chromatin Interaction Analysis Using Paired-End Tag Sequencing (ChIA-PET) is an established method to map protein-mediated chromatin interactions. A limitation, however, is that it requires a hundred million cells per experiment, which hampers its broad application in biomedical research, particularly in studies in which it is impractical to obtain a large number of cells from rare samples. To reduce the required input cell number while retaining high data quality, we developed an in situ ChIA-PET protocol, which requires as few as 1 million cells. Here, we describe detailed step-by-step procedures for performing in situ ChIA-PET from cultured cells, including both an experimental protocol for sample preparation and data generation and a computational protocol for data processing and visualization using the ChIA-PIPE pipeline. As the protocol significantly simplifies the experimental procedure, reduces ligation noise, and decreases the required input of cells compared to previous versions of ChIA-PET protocols, it can be applied to generate high-resolution chromatin contact maps mediated by various protein factors for a wide range of human and mouse primary cells. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation and data generation Support Protocol: Bridge linker preparation Basic Protocol 2: Data processing and visualization.

ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.

Core regulatory circuitries in defining cancer cell identity across the malignant spectrum

Gene expression programmes driving cell identity are established by tightly regulated transcription factors that auto- and cross-regulate in a feed-forward manner, forming core regulatory circuitries (CRCs). CRC transcription factors create and engage super-enhancers by recruiting acetylation writers depositing permissive H3K27ac chromatin marks. These super-enhancers are largely associated with BET proteins, including BRD4, that influence higher-order chromatin structure. The orchestration of these events triggers accessibility of RNA polymerase machinery and the imposition of lineage-specific gene expression. In cancers, CRCs drive cell identity by superimposing developmental programmes on a background of genetic alterations. Further, the establishment and maintenance of oncogenic states are reliant on CRCs that drive factors involved in tumour development. Hence, the molecular dissection of CRC components driving cell identity and cancer state can contribute to elucidating mechanisms of diversion from pre-determined developmental programmes and highlight cancer dependencies. These insights can provide valuable opportunities for identifying and re-purposing drug targets. In this article, we review the current understanding of CRCs across solid and liquid malignancies and avenues of investigation for drug development efforts. We also review techniques used to understand CRCs and elaborate the indication of discussed CRC transcription factors in the wider context of cancer CRC models.

The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis

It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.