Masters candidate in Biostatistics, Torri Simon, will present:

“Identifying Differentially Expressed Proteins Using Peptide-Level Data: An Application of Multiple Methods”

Plan B Adviser: Lin Zhang

Abstract: The identification and quantification of proteins in living organisms enables advanced research into associations between protein function and disease development. The latest mass spectrometry (MS) technology can infer relative protein expression for multiple samples by detecting and combining sets of peptide intensities or spectral counts. Conventional data analysis attempts to identify proteins that are differentially expressed between diseased and healthy individuals by performing statistical tests on the combined peptide-intensities. However, research suggests that this causes information loss since peptides belonging to a given protein may behave very differently, having varied impact on protein and disease relationship. Alternatively, expression may be analyzed at the peptide-level and later combined to get significance at the protein-level. This study aimed to compare the proteomic profiles of lung sarcoidosis patients to control patients using differential expression analysis methods that retain peptide-level information. New methods were adapted from those commonly used for genome-wide association studies and existing methods were tested.

Presented via Zoom: https://umn.zoom.us/j/98117420607
Meeting ID: 981 1742 0607