About Us

Welcome to the laboratory of Saurabh Sinha at the University of Illinois, Urbana-Champaign.

Our research focuses on computational approaches to problems in molecular biology. In particular, we are interested in exploring gene regulation, mostly in metazoan genomes. We approach the subject through an integrated analysis of DNA sequence, gene expression, and epigenetic data. We also strive to understand how sequences involved in gene regulation have evolved, and how such evolutionary dynamics may inform the discovery of novel regulatory sequences. Broadly speaking, our work falls in the areas of regulatory and comparative genomics.

Principal Investigator: Saurabh Sinha.


Graphical models help identify major transcriptional regulators of drug response variation

Hanson, C., Cairns, J., Wang, L., & Sinha, S. (2018). Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res, 28(8), 1207-1216. doi:10.1101/gr.227066.117. [Free full text]

A scaled kernel SVM model helps improve gene signature matching across cell lines

Xiao, J., Blatti, C., & Sinha, S. (2018). SigMat: a classification scheme for gene signature matching. Bioinformatics, 34(13), i547-i554. doi:10.1093/bioinformatics/bty251. [Free full text]

Multi-species analysis reveals homologous functional groups

Saul, M. C., Blatti, C., Yang, W., Bukhari, S. A., Shpigler, H. Y., Troy, J. M., . . . Sinha, S. (2018). Cross-species systems analysis of evolutionary toolkits of neurogenomic response to social challenge. Genes Brain Behav, e12502. doi:10.1111/gbb.12502. [Abstract]

Random walks on biological networks help understand gene sets

C. A. Blatti and S. Sinha (2016) "Characterizing Gene Sets using Discriminative Random Walks with Restart on Heterogeneous Biological Networks", Bioinformatics", 32(14):2167-75. . [Full text].

Accounting for DNA shape at binding sites helps predict gene expression 

Pei-chen Peng and S. Sinha (2016). "Quantitative modeling of gene expression using DNA shape features of binding sites.", Nucleic Acids Res. 44(13):e120. [Free full text]

How "big" is genomics going to get?

Z. D. Stephens, S. Y. Lee, F. Faghri, R. H. Campbell, C. Zhai, M. J. Efron, R. Iyer, M. C. Schatz*, S. Sinha*, G. E. Robinson* (2015). *Co-corresponding authors. "Big Data: Astronomical or Genomical?", PLoS Biology, 13(7):e1002195. [Free full text]

News coverage: Forbes magazine, NatureScience News, GenomeWeb magazine, Washington Post