Genomic methods have broadly enabled deep characterization of a variety of biological processes. There are several areas where we are interested in developing new methods and in performing new analyses.

Genomic studies have often been limited by sample availability.  The end result is that published genomic studies are often much less representative of the disease or population they claim to represent.  We have been working to develop methods to correct for various biases and skewings of the data to improve the understandings that can come from genomic analyses (i.e., Mendiratta et al, Nature Communications, 2022, Mendiratta et al, Med, 2024).

We are also very interested in re-analyzing available data to ask specific questions. Although millions of possible questions could be asked of genomic data, multiple-hypothesis limits the information can be extracted from available data sets when large-scale data mining is pursued. In contrast, available genomic data is well-powered to test pre-specified and well-formulated hypotheses that are based upon other pieces of data (i.e., Trogdon et al, npj Systems Biology, 2024; Stites et al, Cell Reports, 2015).

New experimental designs using genomic methods, in turn requiring new computational methods, are also of interest, particularly when done to advance research problems we are pursuing in systems biology, synthetic biology, and disease biology (i.e., Mendiratta G, bioRxiv 2021; Trogdon et al, Accepted, 2024).