ProfAaron Darling

Comparative genomics

Designing and developing scalable computational algorithms to identify the complete set of genetic differences between two or more organisms and relating these differences to aspects of the organism biology. Associating genomic changes to phenotypic changes.

Computational metagenomics

The vast majority of life on the planet is microbial, and most of it can not be studied by laboratory cultivation. Metagenomics involves DNA sequencing of microbes taken directly from the environment. Current metagenomic methods require advanced computational, statistical, and machine learning techniques to identify the organisms present in a sample and characterize their potential for encoding functional proteins.

Genome evolution

Life is thought to have existed on earth for at least four billion years. During this time, evolution has shaped the genomes of modern organisms. Using statistical methods such as continuous time Markov chain models we can infer the history of genome evolution that led to modern organisms. I am interested in applying methods from statistical mechanics and financial market modeling to develop scalable computational methods to reconstruct evolutionary histories.

Next-generation DNA sequencing

DNA is fundamentally a molecule that encodes digital information. New sequencing technology enables us to read this biological information en masse so that it can be analyzed computationally. I am interested in designing sequencing experiments and protocols in ways that maximize the useful information obtained about a biological system.