Label-Free Shotgun Proteomics Reveal Pathways Pivotal in Pre-neoplastic Prostate Development

摘要

Quantitative analysis of proteomes by spectral counting and area-underthe- curve approaches determined that proteins involved in extra-cellular signaling (adherens junctions and focal adhesion) gene ontological terms were enriched. The best approach to performing spectral count analysis for proteomic data is through leveraging multiple distribution models to arrive at the appropriate hypothesis tests. MultiSpec is a multipronged statistical approach that leverages three independent statistical platforms: Beta-Binomial, edgeR and BaySeq. Even though dynamic exclusion parameters were optimized for spectral count quantitation, MaxLFQ was able to utilize spectral feature alignment and AUC quantitation to achieve deeper proteomic analysis than what is possible through this spectral count approach. By employing both spectral count and AUC approaches in discovery shotgun proteomics experiments, Gene Ontology Analyses are able to identify differentially expressed pathways for future experiments.