The expertise developed by the lab in yeast experimental genetics and computational genomics has significant applications to drug discovery and drug development efforts. On the pure computational side, projects are underway that strive to tease out meaningful trends from complex genomic data that correspond to disease associations. Other ongoing projects have developed methods that allow engineering of partially humanizing yeast, an organism amenable to high throughput screening. The lab is developing technology based on emulsion PCR from single cells and parallel sequencing to efficiently measure the phenotypic effects of combinatorial perturbations, enabling efficient discovery of protein and genetic interactions. The lab is also developing new methodologies for substantially accelerated generation of DNA sequence through a modification of existing parallel sequencing approaches, thus significantly reducing time and costs, leading to benefits particularly for any project requiring large scale DNA sequencing.

Dr. Roth:

• Has developed a technology based on emulsion PCR from single cells and parallel sequencing to efficiently measure the phenotypic effects of combinatorial perturbations, enabling efficient discovery of protein and genetic interactions.
• Uses machine learning approaches and ‘omic data integration to correlate genes with biological function.
• Has developed the “Green Monster” technology that enables the efficient engineering of yeast cells at dozens of loci using fluorescence protein reporters and fluorescence-activated cell sorting.
• Is predicting and testing potentially synergistic drug combinations.
• Integrating genomic data to better identify alleles associated with disease, thus facilitating a personalized medicine approach.

Dr. Roth’s laboratory combines computational genomics with experimental genetic technology aimed at understanding the complex repertoire of gene networks.  Experimental interests have focused on development of parallel-sequencing based technologies for revealing genetic interactions in the model organism S. cerevisiae and revealing protein interactions in humans. Computational interests have included integrating diverse data sets to infer gene function, using large-scale protein and genetic interaction networks to define pathways and their order of action, and on identifying synergistic drug combinations. 
 

Biological Mechanisms and Pathways

• Alternative splicing •
• Computational genomics •
• Gene function •
• Genetic interactions •
• Genetic pathways •
• Protein interactions •
• Signaling network
•  

Disease Mechanisms

• Complex disease
•  

Research Tools and Instrumentation

• DNA sequencing •
• Experimental yeast genetics •
• Genetic pulldown •
• High Throughput Screening •
• Humanized yeast •
• Methods development •
• Probabilistic decision tree •
• Technology development •
• Yeast
•  

Therapeutic Discovery Tools and Assays

• High Throughput Screening •
• Technology development •
• Yeast
•