Sunduz Keles

Professor of Statistics

2124 Genetics-Biotechnology Center,, 608.265.4384


Biostatistics & Medical Informatics, Statistics


PhD (2003), University of California-Berkeley

Research Interests

Developing and applying statistical methods for problems that arise in genome biology

Lab Website

Representative Awards

  • 2003 Jiann-Ping Hsu Award for Excellence and Scholarship, University of California, Berkeley.
  • 2006 David Byar Young Investigator Award, Biometrics Section of the American Statistical Association.
  • 2007 PhRMA Foundation Award in Informatics
  • 2012 H.I. Romnes Faculty Fellowship, University of Wisconsin-Madison


Understanding how the genome is regulated is a gateway to understanding developmental and disease mechanisms at a molecular level. Research in Keles group centers around innovating statistical and computational methods for the analysis of high throughput sequencing data (e.g., ChIP-seq, eCLIP-seq, Hi-C) for studying genome regulation. Current research themes include (1) developing statistical methods and software to leverage data from high throughput sequencing technologies such as ChIP-seq, Hi-C to study repetitive regions of the genome; (2) developing integrative analysis frameworks for leveraging heterogeneous public data (epigenome, scRNA-seq, GWAS data) to elucidate impacts of noncoding association SNPs on genome regulation.

    Representative Publications  (Google Scholar | PUBMED)

    • Zheng, Y., Ay, F. & Keles, S. Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies. eLife 8, doi:10.7554/eLife.38070 (2019).
    • Shin, S., Hudson, R., Harrison, C., Craven, M. & Keles, S. atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding. Bioinformatics (Oxford, England) 35, 2657-2659, doi:10.1093/bioinformatics/bty1010 (2019).
    • Rojo, C., Zhang, Q. & Keles, S. iFunMed: Integrative functional mediation analysis of GWAS and eQTL studies. Genet Epidemiol, doi:10.1002/gepi.22217 (2019).
    • Zhang, Q. & Keles, S. An empirical Bayes test for allelic-imbalance detection in ChIP-seq. Biostatistics 19, 546-561, doi:10.1093/biostatistics/kxx060 (2018).
    • Zuo, C., Chen, K., Keles, S.  A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets. Journal of Computational Biology 24, 472-485, (2017).
    • Kreimer, A. et al. Predicting gene expression in massively parallel reporter assays: A comparative study. Hum Mutat 38, 1240-1250, doi:10.1002/humu.23197 (2017).