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Code annotations are used mainly in modern Java software, and their primary purpose is to configure metadata into programmable elements such as methods and classes. The Annotation Sniffer is a stable tool that analyses Java source code and generates a JSON report with code annotations metrics values. In other words, it measures metadata configuration in the Java source file. The Annotation Visualizer is a tool built as a web application to render the visualization using this report as input. In this work, we developed a tool that integrates the Annotation Visualizer as a plugin for the IntelliJ IDEA, a popular Java IDE. This integration allows developers to visualize their current project opened in the IDE without switching to an external environment. With an IDE plugin, developers can potentially benefit from the visualization and the metrics values it can display. Furthermore, IDEs have a marketplace that eases obtaining the tool and increases the probability that the developers might use it.

Protein functional annotation consists in associating proteins with textual descriptors elucidating their biological roles. The bulk of annotation is done via automated procedures that ultimately rely on annotation transfer. Despite a large number of existing protein annotation procedures the ever growing protein space is never completely annotated. One of the facets of annotation incompleteness derives from annotation uncertainty. Often when protein function cannot be predicted with enough specificity it is instead conservatively annotated with more generic terms. In a scenario of protein families or functionally related (or even dissimilar) sets this leads to a more difficult task of using annotations to compare the extent of functional relatedness among all family or set members. However, we postulate that identifying sub-sets of functionally coherent proteins annotated at a very specific level, can help the annotation extension of other incompletely annotated proteins within the same family or functionally related set. As an example we analyze the status of annotation of a set of CAZy families belonging to the Polysaccharide Lyase class. We show that through the use of visualization methods and semantic similarity based metrics it is possible to identify families and respective annotation terms within them that are suitable for possible annotation extension. Based on our analysis we then propose a semi-automatic methodology leading to the extension of single annotation terms within these partially annotated protein sets or families.