This site was searched in July 2019 for tools using the words ‘kinase’, ‘phosphorylation’, ‘phospho’, or ‘phosphatase’. The OMICtools resource ( ) is a manually curated collection of bioinformatics tools. We perform some benchmarking comparisons to determine the best tool available and assess usability of the tools from the standpoint of typical biologists or clinicians. Therefore, this article aims to provide a comprehensive collection of resources that can be used to gain insights from phosphoproteomic data, including knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. Furthermore, there is no comprehensive list of tools to aid those using phosphoproteomic data in their research. īiological and clinical scientists are in the best position to extract biologically and clinically relevant findings from phosphoproteomics data, however, they are rarely consulted for tool design input or requested to test the final product. However, there has been little validation of the methods and only one benchmarking study comparing a few of the methods has been published. ![]() For example, inference of kinase activity based on the observed phosphorylation of its substrates is a powerful application of phosphoproteomics profiling, and multiple methods have been developed to address this need. Although newly developed tools are usually compared to similar, previously published tools, comparisons often do not include real-world, biological use-cases. In addition, many tools have overlapping functions but differ in underlying knowledge bases, algorithms, input and output format of data, accessibility, advantages, limitations, and maintenance. However, each of these tools exists as a silo without connection to tools with complementary functions. Although analyses in the first step are typically performed by the proteomics cores using standardized computational tools, those in the second step require and can benefit from active involvement of biologists and clinicians.Ī vast array of resources and tools are available to facilitate the interpretation of phosphopeptide identification and quantification results. The second step aims to translate phosphopeptide identification and quantification results into novel biological and clinical insights. The first step includes the identification, phosphosite localization, and quantification of phosphopeptides. Phosphoproteomics data analysis involves two major steps. While kinase signaling has been studied for over 100 years using a variety of experimental methods, the recent generation of mass spectrometry-based phosphoproteomic profiling allows for an unprecedented global exploration of phosphorylation. Therefore, detailed knowledge of the kinase signaling process is essential for the understanding of diseases and the development of new therapies. Because its dysregulation contributes to many diseases, numerous clinical trials have been performed with kinase inhibitors resulting in over 50 FDA-approved small molecules and targeted antibodies. ![]() Kinase signaling, the reversible enzymatic addition of a phosphate group to a substrate, is an essential part of cellular activity. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling.
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