Biology subjects

Behjati Ardakani, F.

Publications and source records attributed to Behjati Ardakani, F..

5 recordsLinked to original sources

bioRxiv2026

EpiATLAS - a reference for human epigenomic research

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease1. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium2 (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

genomics↗

bioRxiv2025

Predicting gene-specific regulation with transcriptomic andepigenetic single-cell data

MotivationAnalysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. ResultsWe present the MetaFR approach to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. Availability and implementationMetaFR is freely available under https://github.com/SchulzLab/MetaFR.

bioinformatics↗

bioRxiv2025

Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci

Coronary artery disease (CAD) is the leading cause of death worldwide. Recently, hundreds of genomic loci have been shown to increase CAD risk, however, the molecular mechanisms underlying signals from CAD risk loci remain largely unclear. We sought to pinpoint the candidate causal coding and non-coding genes of CAD risk loci in a cell type-specific fashion. We integrated the latest statistics of CAD genetics from over one million individuals with epigenetic data from 45 relevant cell types to identify genes whose regulation is affected by CAD-associated single nucleotide variants (SNVs) via epigenetic mechanisms. Applying two statistical approaches, we identified 1,580 genes likely involved in CAD, about half of which have not been associated with the disease so far. Enrichment analysis and phenome-wide association studies linked the novel candidate genes to disease-specific pathways and CAD risk factors, corroborating their disease relevance. We showed that CAD-SNVs are enriched to regulate gene expression by affecting the binding of transcription factors (TFs) with cellular specificity. Of all the candidate genes, 23.5% represented non-coding RNAs (ncRNA), which likewise showed strong cell type specificity. We conducted a proof-of-concept biological validation for the novel CAD ncRNA gene IQCH-AS1. CRISPR/Cas9-based gene knockout of IQCH-AS1, in a human preadipocyte strain, resulted in reduced preadipocyte proliferation, less adipocyte lipid accumulation, and atherogenic cytokine profile. The cellular data are in line with the reduction of IQCH-AS1 in adipose tissues of CAD patients and the negative impact of risk alleles on its expression, suggesting IQCH-AS1 to be protective for CAD. Our study not only pinpoints CAD candidate genes in a cell type-specific fashion but also spotlights the roles of the understudied ncRNA genes in CAD genetics.

bioinformatics↗

bioRxiv2022

The adapted Activity-By-Contact-model for enhancer-gene assignment and its combination with transcription factor affinities in single cell data

Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model (Fulco et al., 2019) which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a genes perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC-model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is no implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data. We demonstrate that the ABC-score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalised ABC-model (gABC) with TF binding information and illustrate an analysis of a single-cell ATAC-seq data set of the human heart, where we were able to characterise cell type-specific regulatory interactions and predict gene expression based on transcription factor affinities. All executed processing steps are incorporated into our new computational pipeline STARE. The software is available at https://github.com/schulzlab/STARE.

bioinformatics↗

bioRxiv2018

Integrative analysis of single cell expression data reveals distinct regulatory states in bidirectional promoters

BackgroundBidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single cell technologies are not yet established, in the context of BPs.\n\nResultsWe performed integrative analyses of novel human single cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles.\n\nConclusionsOur results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

genomics↗