Functional annotation and biological interpretation of proteomics data

Abstract Proteomics experiments often generate a vast amount of data. However, the simple identification and quantification of proteins from a cell proteome or subproteome is not sufficient for the full understanding of complex mechanisms occurring in the biological systems. Therefore, the functional annotation analysis of protein datasets using bioinformatics tools is essential for interpreting the results of high-throughput proteomics. Although large-scale proteomics data have rapidly increased, the biological interpretation of these results remains as a challenging task. Here we reviewed basic concepts and different programs that are commonly used in proteomics data functional annotation, emphasizing the main strategies focused in the use of gene ontology annotations. Furthermore, we explored the characteristics of some tools developed for functional annotation analysis, concerning the ease of use and typical caveats on ontology annotations. The utility and variations between different tools were assessed through the comparison of the resulting outputs generated for an example of proteomics dataset.