We study paralogous factors with similar motifs, but different genomic targets and regulatory roles


We use gcPBM assays to identify differences in DNA-binding specificity between paralogous transcription factors


We use weighted regression to model differential specificity of paralogous TFs


Check out our web server:


We find that differences in intrinsic specificity partly explain differential in vivo binding


We develop quantitative models of TF-DNA binding based on sequence and shape features


We develop methods to predict the effects of non-coding genetic variants on TF binding and gene expression

Welcome to the Gordan Lab

We develop quantitative computational and experimental approaches to identify and characterize transcriptional regulatory regions in the human genome. We combine machine learning techniques and high-throughput assays. Focusing on transcription factors (TFs) that control cell proliferation and differentiation, our goals are to understand: (1) how these regulatory proteins interact with each other to select their unique targets across the genome, and (2) how this regulation is disrupted in diseased cells due to non-coding mutations and TF overexpression.

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Check out our new iMADS web server. It includes quantitative models of specificity and differential specificity for human TFs, easy-to-access genome-wide predictions, and an online tool to make predictions for custom sequences. Feedback is always welcome: Enjoy!