Computational Biology

Computational biology is integral to the research being conducted in every lab in the Department of Genetics and Genome Sciences. The development and implementation of computational pipelines are now essential components for genomics research, including epigenomics, single cell genomics, transcriptomics, and other high-throughput studies. Computational approaches are being applied every day by the labs in our department to better understand genome structure and genetic diseases, with a particular focus on cancer genetics.

Just a few of the many examples of the computational and bioinformatic approaches ongoing in our department include:

Fulai Jin is developing bioinformatic and machine-learning tools to improve the robustness and affordability of high-resolution 3D genome analysis with both bulk and single cell Hi-C data. The laboratory of Yan Li is combining single cell multi-OMIC and functional genomic approaches to study diabetes and stem cell biology. Research Ashleigh Schaffer’s lab is focused on understanding how RNA processing and mRNA isoform balance influences brain health and mediates neurological disease. Tony Wynshaw-Boris’ lab uses whole genome approaches to DNA sequence variants, RNA-seq for gene expression, epigenetics and single cell sequencing to investigate human developmental neurogenetic disorders. The lab of Chen-Han Wilfred Wu is working with germline genome, somatic genome, transcriptome, and epigenome data, together with clinical electronic records to study urological diseases/conditions. Helen Miranda’s lab uses a multi-OMICs approach to identify transcriptional regulatory networks of neurodegenerative disease-relevant transcription factors using human stem cell-derived neurons. David Buchner’s lab is developing new methods to discover how genetic interactions contribute to metabolic diseases such as obesity and type 2 diabetes as well as applying genomic approaches to investigate insulin signaling and glucose regulation in adipocytes.

A particular focus of our department is the use of computational tools to discover new insights into cancer development and treatment. The laboratory of Zhenghe Wang collaborates with others to perform proteomics, genome, epigenetic, transcriptome, and single-cell analyses to understand how oncogenic mutations drive tumorigenesis. Chris McFarland's group designs forward evolutionary simulations of cancer, and analyzes the many high-dimensional public datasets (cancer genomic sequencing, multi 'omic, DNA barcoding) using these simulations, machine learning, and traditional statistical approaches. Tom LaFramboise’s laboratory both develops and applies existing computational methodology to understand the role of the human genetic variation - inherited and somatic - in the development and trajectory of cancer, as well as the impact of the microbiome on these processes. Berkley Gryder’s lab uses protein-directed 3D loops (AQuA-HiChIP) as a way to study biomolecular condensates (liquid droplets that envelop enhancers and genes), to build 3D maps of cancer epigenomes, and to understand drugs in light of the 3D genome folding events they induce. Dr. Yang Liu’s lab uses computational and statistical methods to analyze cancer profiling datasets, aiming to gain novel insights into cancer etiology and pinpoint new candidates of cancer therapeutic targets.