Poster abstracts
Poster number 42 submitted by Owen E. Branson
A multi-platform approach for the analysis of shotgun proteomics data
Owen E. Branson (The Ohio State Biochemistry Program), Michael A. Freitas (Department of Molecular Virology, Immunology and Medical Genetics)
Abstract:
The rapid development of mass spectrometry technologies has solidified shotgun proteomics as the most powerful analytical platform for large-scale proteome interrogation. The ability to map and determine differential expression profiles of the entire proteome is the ultimate goal of shotgun proteomics. Our novel approach to determine differential expression from spectral counts in shotgun proteomics leverages multiple statistical platforms that are routinely used to analyze RNA sequencing data. To remove bias associated with a single statistical approach, a single ranked list of differentially expressed proteins is assembled by means of comparing edgeR and DESeq q-values directly with the false discovery rate calculated by baySeq. Finally this statistical approach is then extended to spectral count data derived from multiple proteomic pipelines. The individual protein identifications and their respective statistics from multiple proteomic pipelines are integrated by collapsing protein groups across proteomic pipelines providing a single ranked list of differentially expressed proteins. Our approach of leveraging multiple search engines allows for in-silico cross-validation of proteomic results and increases the depth of the experiment with the ultimate goal of understanding the biological system of interest.
Keywords: Proteomics, Mass Spectrometry