Miranda Darby

Biography

Miranda M. Darby, Ph.D., is assistant professor of bioinformatics. She teaches a graduate course in Leadership and Project Management in the Life Sciences and is contributing to the development of the BIFX curriculum. Darby earned a bachelor's degree in biology from Carleton College and a Ph.D. in molecular biology and genetics from the Johns Hopkins School of Medicine. She gained practical experience in bioinformatics while completing a postdoctoral fellowship at the Johns Hopkins School of Medicine, where she developed and implemented bioinformatics tools to study the expression of repetitive elements (repeated sequences that make up roughly 50 percent of the genome) and also to identify concerted changes in the expression of functionally-related genes across individuals with schizophrenia, bipolar disorder, and major depression. She has mentored undergraduate researchers studying subjects ranging from the mechanisms that regulate RNA transcription in yeast to the characterization of novel mRNA isoforms expressed in the human brain. Darby has published articles on the function and regulation of a non-canonical RNA transcription termination pathway in yeast; altered gene expression in psychiatric disease; and novel mRNA isoforms expressed in the human brain that are generated by splicing of repetitive RNA sequences. Her current research focuses on the development of computational methods to identify and quantify the expression of novel RNAs using whole genome RNA sequencing. She also serves as a reviewer for peer-reviewed journals.

Research and teaching interests

I am interested in discovering the function of the 97 percent of the human genome that is not annotated as protein coding genes. While protein binding sites and other functional elements are increasingly being mapped throughout the regions between genes and within introns, less is known about RNA transcription in the vast regions of the genome that have traditionally been termed “junk DNA”. The advent of whole genome RNA sequencing allows us to “see” for the first time that there are innumerable transcripts that are not annotated gene isoforms or known non-coding RNAs. Better analysis methods are needed to characterize and quantify these RNAs as well as to gain insight into their potential functions. Since methods for generating and analyzing new types of data are still evolving, my other focus is on teaching students (and researchers at all levels) to assess the quality of new data and of the new methods used to analyze it in the absence of established norms.