Hannenhalli lab has relocated to Cancer Data Science Lab, NCI, NIH on Sep 4th, 2019. This site is no longer maintained.


Within the broad field of computational biology, we focus on eukaryotic gene regulation and its evolution. We develop computational approaches to harness the huge amount of biological data (genomes, epigenomes, transcriptomes, proteomes, etc.) to answer specific biological questions pertaining to these domains. We are also involved in exploiting massive amounts to clinical and molecular data in cancer to identify genetic interactions and dysregulations relevant to cancer. Please refer to our publications to get a better sense of what we do. As you can tell, our interests are broad and we are easily distractable bunch. Collaboration ideas are always welcome. Here are representative recent and current projects:

Transcriptional regulatory mediators of genotype-phenotype relationship

While numerous association studies (eQTL and GWAS) have revealed polymorphisms associated with gene expression and diseases, clinical progress is hampered by our lack of understanding of functional consequences of polymorphisms (SNP), especially, the non-coding SNPs. We are developing integrative strategies to identify SNPs causaly linked to gene expression and phenotypic changes. A long-term goal in this context is to be able to predict the disease risks for specific haplotypes and personalize treatments for genetic diseases.

Networks of transcriptional enhancers

Recent ENCODE data has revealed numerous distal enhancers in dozens of cell lines. Analysis of these enhancers across cell lines reveals a spatially clustered enhancer groups with correlated activity, in tunr regulating correlated gene expressions of functionally linked genes. We are further characterizing these enhancer clusters and investigating their relationships with conditional activation of cis elements.

Evolution of transcriptional regulation

Evolution of organismal complexity is mediated in significant part by the changes in transcriptional regulation. In this context, we are interested in (1) the role of gene duplication and divergence, as it provides a crucial fodder for evolutionary innovation. (2) the role of transcriptional network rewiring during evolution (3) intraspecific variability in regulatory signals and how it reveals selection and how it relates to phenotypic variability.

Characterizing transcription factor DNA interactions

Transcription factors (TF) bind to short and often degenerate DNA motifs and as such, lack sufficient information to accurately recognize the binding sites. A fundamental question is - what provides the requisite specificity of TF-DNA interactions? Is knowing the chromatin acessibility and in vitro binding specificty of a TF sufficient to predict its in vivo binding? What is relationship between TF binding and various epigenomic marks? Are there functional distinct subclasses of binding sites for a particular TF?

Genetic Interactions and gene dysregulation in Cancer

Exploiting massive amounts of molecular and clinical in tens of thousands of cancers across dozens of cancer type, we are developing approaches, jointly with Ruppin lab, to mine the data to learn about hidden genetic interactions and dysregulation of genetic networks, at the level of transcription as well as signaling, relevant to cancer.

Regulation and consequences of alternative splicing and polyadenylation

In the context to specific human diseases and aging, we are investigating the role of epigenomics in determining splicing and polyadenylation and the role of polymorphisms in related cis elements in specific diseases.