Journal of Proteome ResearchBioinformaticsPeer-reviewed journal article
Using Multilayer Heterogeneous Net-works to Infer Functions of Phosphorylated Sites
Abstract
1Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSite Plus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles. For table of contents onlyWe confirm that the eTOC figure contains original material drawn by the authors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/266072v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@f11983org.highwire.dtl.DTLVardef@c21d48org.highwire.dtl.DTLVardef@1078ecorg.highwire.dtl.DTLVardef@1730d96_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- Citations
- 4
- FWCI
- 0.31
- Field percentile
- 47th percentile
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