Current Research

Next-generation sequencing (NGS) technologies have revolutionized our ability to carry out large-scale adaptive immune receptor repertoire sequencing (AIRR-seq) experiments. AIRR-seq is increasingly being applied to gain insights into immune responses in healthy individuals and those with a range of diseases, including autoimmunity, infection, allergy, cancer and aging. As NGS technologies improve, these experiments are producing ever larger datasets, with tens- to hundreds-of-millions of BCR sequences, requiring the development of new computational methods to manage and analyze these “Big Data”. For an overview, please check out our review.

We have developed many widely used computational methods for AIRR-seq data processing and analysis. These methods are available to the wider scientific community through the Immcantation framework, which provides a start-to-finish analytical ecosystem for high-throughput AIRR-seq datasets, with a focus on B cell receptor (BCR) repertoire profiling. Working in close collaboration with basic experimental and clinical groups, we have been applying our methods to gain biological insights in several systems, including: infection (HIV, Salmonella, West Nile virus), vaccination (influenza), allergy (allergic rhinitis, atopic asthma) and autoimmune disease (Multiple Sclerosis, Myasthenia Gravis). We are also active members of the AIRR Community.

Individual variations in immune status and function produce significant heterogeneity in infection and vaccination responses. For example, West Nile virus infection is usually asymptomatic, but can cause severe neurological disease and death, particularly in older patients. Our research leverages recent advances in immune profiling methods to characterize diverse states of human immune system (in health and disease, and following infection and vaccination). We have developed several computational methods for large-scale genetic network modeling, including:

• QuSAGE, which quantifies pathway activity from high-throughput transcriptional profiling data while accounting for gene-gene correlations
• LogMiNeR, which leverages prior knowledge networks to improve model interpretability in the analysis of high-throughput transcriptional profiling data.
• SPEC, which predicts the specific cellular source (e.g., B cells, T cells, etc.) of a gene expression signature using data from total PBMCs
• TIDAL, which integrates genome-wide expression kinetics and time-dependent promoter analysis to reconstruct transcriptional regulatory networks

For a complete list, check out our software page.
A major biological focus area for this research is the response to influenza infection and vaccination. As part of the multi-institutional Program for Research on Immune Modeling and Experimentation (PRIME), we are developing data-driven models for the response of multiple human cell types to infection with different strains of influenza (including the infamous 1918 pandemic strain). We also study influenza vaccination responses as part of the NIH/NIAID Human Immunology Project Consortium (HIPC).