Dina Ruud Aronsen

• Baydar, Damla Övek; Rauluseviciute, Ieva; Aronsen, Dina Ruud; Blanc-Mathieu, Romain; Bonthuis, Ine & de Beukelaer, Herman [Show all 34 contributors for this article] (2025). JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Research (NAR). ISSN 0305-1048. 54(D1), p. D184–D193. doi: 10.1093/nar/gkaf1209. Full text in Research Archive Show summary

  Abstract JASPAR (https://jaspar.elixir.no/) is an open-access database that has provided high-quality, manually curated, and non-redundant DNA binding profiles for transcription factors (TFs) as position frequency matrices (PFMs) for over 20 years. We expanded the CORE (306 new profiles, 12% increase) and UNVALIDATED (433, 60% increase) collections with new PFMs and updated 13 existing profiles. We updated the TF binding site predictions and genome tracks for eight species. TF binding profile clusters and familial TF binding sites were updated accordingly. We integrate the inMOTIFin software to easily simulate regulatory sequences using JASPAR PFMs. To enrich TFs’ annotations, we provide scientific literature-based human TF target information. Notably, this release features a deep learning (DL) collection, providing a paradigm shift in modeling and characterizing TF–DNA interactions with 1259 BPNet models trained on Homo sapiens ENCODE chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets from 240 TFs and interpreted to reveal predictive motif patterns for the models. The motifs associated with the same TF were clustered to provide a summary of the binding properties, resulting in 240 primary and 113 alternative motif patterns in the DL collection. The JASPAR 2026 collections lay a foundation for future endeavors in genomic research, serving the scientific community in uncovering the mechanisms of gene regulation.

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• Aronsen, Dina Ruud (2022). Cis-regulatory signatures for improved identification and stratification of breast cancer subtypes. Full text in Research Archive Show summary

  The project involves the implementation of a modified version of the CAGE (Cap Analysis of Gene Expression) methodology, named SLIC-CAGE (Super-Low Input Carrier CAGE). The method is used for detection of RNA polymerase II TSSs allowing the discovery and mapping of active promoters and enhancers for transcription initiation signatures. The goal of the project is to identify regulatory regions active in breast cancer to define cis-regulatory signatures of breast cancer subtypes, which will aid in better understanding of the underlying biology and stratification of breast cancer patients.

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