In the past few years, the Plant Protein Phosphorylation Database (P3DB, http://p3db.org) has become one of the most significant in vivo data resources for studying plant phosphoproteomics. We have substantially updated P3DB with respect to format, new datasets and analytic tools. In the P3DB 3.0, there are altogether 47 923 phosphosites in 16 477 phosphoproteins curated across nine plant organisms from 32 studies, which have met our multiple quality standards for acquisition of in vivo phosphorylation site data. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein–protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data—all of which provides context for the phosphorylation event. In addition, P3DB 3.0 incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, the new P3DB reflects a community-based design through which users can share datasets and automate data depository processes for publication purposes. Each of these new features supports the goal of making P3DB a comprehensive, systematic and interactive platform for phosphoproteomics research.
Prolactinoma is the most common intracranial neoplasms. Although prolactinoma is always treated with anticarcinogen, many patients recurrence after curing. This indicates that we need to identify a new mechanism for the treatment of prolactinoma. In order to recognize new biomarkers, we identify the differentially expressed genes (DEGs) by the microarray. A total of 86 DEGs are identified including 35 up-regulated genes and 51 down-regulated genes. The set of DEGs can distinguish tumor samples and normal samples significantly. The genes are mainly enriched in 33 Go terms and 2 kegg pathways associated with prolactinoma. In order to recognize the function of DEGs, we import these genes into protein-protein interaction network to analyze these genes. For example, MDM2, LYN, CDH1, GH1, ACTG1 and FUS play an important role in prolactinoma. In summary, the gene set we recognize can provide potential effect for treatment of prolactinoma..
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