Chang M. Yun

All Publications

• JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic acids research Ovek Baydar, D., Rauluseviciute, I., Aronsen, D. R., Blanc-Mathieu, R., Bonthuis, I., de Beukelaer, H., Ferenc, K., Jegou, A., Kumar, V., Lemma, R. B., Lucas, J., Pochon, M., Yun, C. M., Ramalingam, V., Deshpande, S. S., Patel, A., Marinov, G. K., Wang, A. T., Aguirre, A., Castro-Mondragon, J. A., Baranasic, D., Chèneby, J., Gundersen, S., Johansen, M., Khan, A., Kuijjer, M. L., Hovig, E., Lenhard, B., Sandelin, A., Vandepoele, K., Wasserman, W. W., Parcy, F., Kundaje, A., Mathelier, A. 2025

  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.

  View details for DOI 10.1093/nar/gkaf1209

  View details for PubMedID 41325984

• Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart. bioRxiv : the preprint server for biology Marderstein, A. R., Kundu, S., Padhi, E. M., Deshpande, S., Wang, A., Robb, E., Sun, Y., Yun, C. M., Pomales-Matos, D., Xie, Y., Nachun, D., Jessa, S., Kundaje, A., Montgomery, S. B. 2025

  Abstract

  Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritize de novo mutations with extreme regulatory effects, we developed FLARE, a context-specific functional genomic model of constraint. FLARE outperformed other methods in prioritizing case mutations from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers near CNTNAP2 that would be missed by alternative approaches. Overall, our findings demonstrate the potential of integrating single-cell maps with population genetics and deep learning-based variant effect prediction to elucidate mechanisms of development and disease-ultimately, supporting the notion that genetic contributions to neurodevelopmental disorders are predominantly rare.

  View details for DOI 10.1101/2025.02.18.638922

  View details for PubMedID 40027628

  View details for PubMedCentralID PMC11870466

• Towards an efficient and robust electrocatalyst for CO2 electroreduction: Promoting effects of polyvinylpyridines on copper Chernyshova, I., Ponnurangam, S., Yun, C., Wang, S., Somasundaran, P. AMER CHEMICAL SOC. 2016

  View details for Web of Science ID 000431460201722

• Poly(4-vinylpyridine) as a new platform for robust CO2 electroreduction Chernyshova, I., Ponnurangam, S., Yun, C., Somasundaran, P. AMER CHEMICAL SOC. 2016

  View details for Web of Science ID 000431905700293

• Robust Electroreduction of CO₂ at a Poly(4-vinylpyridine)-Copper Electrode CHEMELECTROCHEM Ponnurangam, S., Yun, C., Chernyshova, I. V. 2016; 3 (1): 74-82

  View details for DOI 10.1002/celc.201500421

  View details for Web of Science ID 000371253500010