Analysis of the landscape of human enhancer sequences in biological databases

Hernández, Juan Mulero; Fernández‐Breis, Jesualdo Tomás

doi:10.1016/j.csbj.2022.05.045

Computational and Structural Biotechnology Journal

2022

DOI: 10.1016/j.csbj.2022.05.045

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Analysis of the landscape of human enhancer sequences in biological databases

Juan Mulero Hernández

Jesualdo Tomás Fernández‐Breis²

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2024

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Cited by 2 publications

References 191 publications

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

Loers,

Vermeirssen

2024

Briefings in Bioinformatics

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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.

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

Loers,

Vermeirssen

2024

Briefings in Bioinformatics

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Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation

Mulero-Hernández,

Mironov,

Miñarro-Giménez

et al. 2024

Nucleic Acids Research

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Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

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Analysis of the landscape of human enhancer sequences in biological databases

Cited by 2 publications

References 191 publications

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

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

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation

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