Speaker:  Dr. Karin Dorman

Iowa State University, Department of Statistics

Improving "*-Seq" -- Applications in Protein-Protein Interactions, Repertoire Sequencing, Allotetraploid Genotyping, and Clustering High-Dimensional Data

A myriad of biological assays, from 2P-seq to X-ChIP-seq (a total of 205 methods listed at Enseqlopedia), have been created in the wake of cheap, high throughput sequencing. These methods produce massive digital readouts, the number of sequenced reads associated with each distinct sequence or feature (e.g., species, variant, gene, promoter, CpG), which are then used to perform statistical inference. There are abundant opportunities to improve the data analysis procedures (pipelines) associated with these assays. To give a taste of the plethora of opportunities in this arena, I discuss ongoing work with six students making demonstrable improvements in four application areas: (1) reproducible protein-protein interaction detection, (2) quantitation of amplicon repertoires, (3) allotetraploid genotyping, and (4) clustering high-dimensional datasets.

This is joint work with Ashish Jain, Roshan Kulkarni, Xiyu Peng, Valeria Velasquez Zapata, Lingnan Yuan, Yudi Zhang (listed alphabetically by last name).