Veneer Is a Webtool for Rapid, Standardized, and Transparent Interpretation, Annotation, and Reporting of Mammalian Cell Surface N-Glycocapture Data

Currently, no consensus exists regarding criteria required to designate a protein within a proteomic data set as a cell surface protein. Most published proteomic studies rely on varied ontology annotations or computational predictions instead of experimental evidence when attributing protein localization. Consequently, standardized approaches for analyzing and reporting cell surface proteome data sets would increase confidence in localization claims and promote data use by other researchers. Recently, we developed Veneer, a web-based bioinformatic tool that analyzes results from cell surface N-glycocapture workflows-the most popular cell surface proteomics method used to date that generates experimental evidence of subcellular location. Veneer assigns protein localization based on defined experimental and bioinformatic evidence. In this study, we updated the criteria and process for assigning protein localization and added new functionality to Veneer. Results of Veneer analysis of 587 cell surface N-glycocapture data sets from 32 published studies demonstrate the importance of applying defined criteria when analyzing cell surface proteomics data sets and exemplify how Veneer can be used to assess experimental quality and facilitate data extraction for informing future biological studies and annotating public repositories.