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NLM Intramural Research Program

NLM’s networks, gene regulation, and chromatin research focuses on diverse aspects of gene regulation, such as chromatin organization, DNA conformation, DNA-protein interaction, DNA regulatory elements, and gene regulatory networks. This area of research includes the role of gene regulation and interaction networks in diseases such as cancer, using big data from new experimental technologies. This often involves the development of new computational approaches and algorithms.
Image of Ivan Ovcharenko, PhD

Ivan Ovcharenko, PhD

Senior Investigator, Computational Biology Branch, NCBI

Ivan Ovcharenko, PhD

  • Accurately predicting disease-causal noncoding mutations
  • Developing deep learning methods for DNA sequence analysis
  • Identifying silencers and super-silencers in the human genome
  • Analyzing gene regulatory networks and regulatory mechanisms
  • Studying the role of 3D chromatin structure in gene regulation
Image of David Landsman, PhD

David Landsman, PhD

Senior Investigator, Computational Biology Branch, NCBI

David Landsman, PhD

  • Conducting research on chromatin structure, epigenetics, and gene expression/regulation
  • Investigating nucleosomal binding proteins of the HMGN family, that stabilizes cell identity
  • Exploring molecular modeling of nucleosomes (with Dr. Anna Panchenko of Queen’s University, Canada
  • Developing the Magic-BLAST tool to accurately map deep sequencing, RNA-seq, and DNA-seq (with Dr. Jean Thierry-Mieg and Dr. Danielle Thierry-Mieg of NLM)
Image of Teresa Przytycka, PhD

Teresa Przytycka, PhD

Senior Investigator, Computational Biology Branch, NCBI

Teresa Przytycka, PhD

  • Exploring data integration
  • Identifying dysregulated pathways in cancers
  • Investigating mutagenic processes and mutagenic signatures in cancers
  • Exploring gene regulatory networks and gene regulation
  • Investigating non-canonical DNA structures
  • Developing AptaSUITE, a framework for analyzing big HT-SELEX data
  • Developing scPopCorn, a method for analyzing single-cell datasets