Carnielli Carolina M, Winck Flavia V, Paes Leme Adriana F
Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências, LNBio, CNPEM, Campinas, Brazil.
Biochim Biophys Acta. 2015 Jan;1854(1):46-54. doi: 10.1016/j.bbapap.2014.10.019. Epub 2014 Oct 31.
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.
蛋白质组学实验常常会产生大量数据。然而，仅对细胞蛋白质组或亚蛋白质组中的蛋白质进行简单鉴定和定量，对于全面理解生物系统中发生的复杂机制是不够的。因此，使用生物信息学工具对蛋白质数据集进行功能注释分析对于解释高通量蛋白质组学的结果至关重要。尽管大规模蛋白质组学数据迅速增加，但对这些结果进行生物学解释仍然是一项具有挑战性的任务。在此，我们回顾了蛋白质组学数据功能注释中常用的基本概念和不同程序，重点强调了使用基因本体注释的主要策略。此外，我们探讨了一些为功能注释分析而开发的工具的特点，涉及易用性以及本体注释方面的典型注意事项。通过比较针对一个蛋白质组学数据集示例生成的结果输出，评估了不同工具之间的实用性和差异。
