Dual data and motif clustering improves the modeling and interpretation of phosphoproteomic data

• DOI: 10.1016/j.crmeth.2022.100167
• Publication Date: 2022-02-14T15:43:36Z
• Authors: Marc Creixell; Aaron S. Meyer
• Keywords: Proteome; Phosphoproteomics; Profiling (computer programming)

Cell signaling is orchestrated in part through a network of protein kinases and phosphatases. Dysregulation kinase widespread diseases such as cancer readily targetable inhibitors. Mass spectrometry-based analysis can provide global view regulation, but mining these data complicated by its stochastic coverage the proteome, measurement substrates rather than kinases, scale data. Here, we implement dual motif clustering (DDMC) strategy that simultaneously clusters peptides into similarly regulated groups based on their variation sequence profile. We show this help to identify putative upstream supply more robust clustering. apply clinical proteomic profiling lung conserved signatures tumorigenicity, genetic mutations, immune infiltration. propose DDMC provides general flexible for phosphoproteomic