EpiRegio: analysis and retrieval of regulatory elements linked to genes
1 July 2020·
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0 min read
Nina Baumgarten
Dennis Hecker
Sivarajan Karunanithi
Florian Schmidt
Markus List
Marcel H. Schulz
Abstract
A current challenge in genomics is to interpret non-coding regions and their role in transcriptional regulation of possibly distant target genes. Genome-wide association studies show that a large part of genomic variants are found in those non-coding regions, but their mechanisms of gene regulation are often unknown. An additional challenge is to reliably identify the target genes of the regulatory regions, which is an essential step in understanding their impact on gene expression. Here we present the EpiRegio web server, a resource of regulatory elements (REMs). REMs are genomic regions that exhibit variations in their chromatin accessibility profile associated with changes in expression of their target genes. EpiRegio incorporates both epigenomic and gene expression data for various human primary cell types and tissues, providing an integrated view of REMs in the genome. Our web server allows the analysis of genes and their associated REMs, including the REM’s activity and its estimated cell type-specific contribution to its target gene’s expression. Further, it is possible to explore genomic regions for their regulatory potential, investigate overlapping REMs and by that the dissection of regions of large epigenomic complexity. EpiRegio allows programmatic access through a REST API and is freely available at https://epiregio.de/.
Type
Publication
Nucleic Acids Research

Authors
Nina Baumgarten
(she/her)
Postdoc
I am a bioinformatician with a diverse background in analyzing genetic and epigenetic data. I have had the chance to work on a variety of projects since I joined the lab, like decoding the effect of non-coding variations, analysing GWAS data, and studying TF binding. Recently, my focus has been shifted toward spatial transcriptomics, which allows me to map genetic findings directly onto tissue architecture. I am motivated by the possibility that these data-driven insights might lead to a better understanding of complex diseases. In my free time, I love spending time in nature for hiking—it’s where the best ideas come to me!

Authors
Dennis Hecker
(he/him)
Postdoc
I am a biologist by training that turned into a bioinformatician for the PhD. Since starting in the SchulzLab, I have been working on analysing a variety of data and their integration with a focus on the epignome, specifically interactions between regulatory regions and genes. I also have a love-hate relationship with chromatin contact data. What keeps me going is the variety of research questions that we work on to know more about human biology and its use in medicine. I am a big fan of Python and coffee.