Comparative genomics, transcriptional regulation, machine learning

Research Overview

In order to adapt to changing environmental conditions, cells control expression of genes, to increase or decrease the amount of specific gene products (protein or RNA) when needed. I’m interested in understanding one of the most common mechanisms to modulate gene expression: regulation of transcription initiation by transcription factors.

Transcriptional factors are proteins that regulate gene expression by recognizing and binding to specific DNA sequences upstream of coding regions. I’m particularly interested in the evolution of transcriptional regulation in bacterial species when facing changes in the base distribution of their genomes. How do transcriptional regulatory elements, namely transcription factors, their binding motifs and regulatory networks, evolve when the genomic background changes significantly? I use comparative genomics and machine learning approaches on experimentally verified transcription factor binding site data with the goal of creating methods and tools to reconstruct regulatory networks and performing their comparative analysis.


  • Kılıç, S., White, E. R., Sagitova, D. M., Cornish, J. P., & Erill, I. “CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria.” Nucleic Acids Research (1 January 2014) 42 (D1): D148-D155 doi:10.1093/nar/gkt1087

  • David Zamorano-Sanchez, Jiunn C. Fong, Sefa Kılıç, Ivan Erill and Fitnat H. Yildiz. “Identification and characterization of VpsR and VpsT recognition sites in Vibrio cholerae. Journal of Bacteriology. Accepted

  • Sefa Kılıç, Pinar Şenkul, Ismail Hakkı Toroslu. “Clustering Frequent Navigation Patterns from Website Logs by Using Ontology and Temporal Information.” Computer and Information Sciences III. 2013


  • “CollecTF: a database of experimentally validated bacterial TF-binding motifs”. 30th Annual Graduate Association of Biological Sciences Symposium. March 2014
  • “A graph-based optimization system for comparative genomics of bacterial regulatory networks”. Great Lakes Bioinformatics Conference. May 2013