376901 Using Deep Sequencing Data to Characterize Immune Repertoires
Diseases, ranging from minor nuisances, such as the common cold, to chronic conditions, such as diabetes, to life threatening problems, such as cancer, regularly impose significant personal and societal costs. Over the last several centuries, major progress has been made in understanding the causes of and developing treatments and / or cures for numerous diseases. Environmental factors are thought to trigger or contribute to many diseases (e.g. Celiac Disease, Type I Diabetes, Narcolepsy, etc.), but in many cases identification of these environmental factors has proven elusive. When properly utilized, deep sequencing technology has the potential to elucidate the environmental factors associated with numerous diseases. The Daugherty Lab at UCSB has previously used a 15-mer random peptide library of 8 * 10^9 members coupled with numerous magnetic and fluorescence activated cell sorting screens to identify environmental factors associated with the pregnancy disorder pre-eclampsia and Celiac Disease.
Here we present an integrated experimental and computational workflow taking advantage of the enormous capacity of deep sequencing to identify immune repertoires. Using just one or two rounds of experimental screening on the 15-mer library and deep sequencing can identify all peptides bound and enriched by a patient's antibodies. A brief description of the experimental methods will be provided, followed by a detailed description of the computational methods to rapidly extract useful data from the deep sequencing results, identify statistically significant features of the peptides, and generate an array of motifs. Representative results for Celiac Disease will illustrate how this approach can, in only a handful of hours on a desktop computer, identify insightful immune repertoire information.