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JavaProtein Dossier ( J PD) is a new concept, database and visualization tool providing one of the largest collections of the physicochemical parameters describing proteins' structure, stability, function and interaction with other macromolecules. By collecting as many descriptors/parameters as possible within a single database, we can achieve a better use of the available data and information. Furthermore, data grouping allows us to generate different parameters with the potential to provide new insights into the sequence-structure-function relationship. In J PD, residue selection can be performed according to multiple criteria. J PD can simultaneously display and analyze all the physicochemical parameters of any pair of structures, using precalculated structural alignments, allowing direct parameter comparison at corresponding amino acid positions among homologous structures. In order to focus on the physicochemical (and consequently pharmacological) profile of proteins, visualization tools (showing the structure and structural parameters) also had to be optimized. Our response to this challenge was the use of Java technology with its exceptional level of interactivity. J PD is freely accessible (within the Gold Sting Suite) at

Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajec- tories, episodes like stops and moves) and diverse movement patterns (e.g., moving clusters, hotel-restaurant-shop-hotel), with formal semantic descriptions. Another issue is how to arrange descriptive data and measures in a Movement Data Warehouse (MDW) for powerful information analyses and reasonable performance. This paper introduces general def- initions for movement segments, movement patterns, their categories and hierarchies. The proposed constructs are se- mantically enriched with references to concepts (categories) and/or instances of these concepts (objects) arranged in dis- tinct hierarchies. Based on these constructs, we propose a semantic multidimensional model for MDW. A case study illustrates the expressiveness of the proposal for analyzing movement data collected via social media and semantically enriched with Linked Open Data (LOD)

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Diamond STING is a new version of the STING suite of programs for a comprehensive analysis of a relationship between protein sequence, structure, function and stability. We have added a number of new functionalities by both providing more structure parameters to the STING Database and by improving/expanding the interface for enhanced data handling. The integration among the STING components has also been improved. A new key feature is the ability of the STING server to handle local files containing protein structures (either modeled or not yet deposited to the Protein Data Bank) so that they can be used by the principal STING components: JavaProtein Dossier (JPD) and STING Report. The current capabilities of the new STING version and a couple of biologically relevant applications are described here. We have provided an example where Diamond STING identifies the active site amino acids and folding essential amino acids (both previously determined by experiments) by filtering out all but those residues by selecting the numerical values/ranges for a set of corresponding parameters. This is the fundamental step toward a more interesting endeavor—the prediction of such residues. Diamond STING is freely accessible at and .

Advances in Semantic Web and Ontologies have pushed the role of semantics to a new frontier: Semantic Composition of Web Services. A good example of such compositions is the querying of multiple bioinformatics data sources. Supporting effective querying over a large collection of bioinformatics data sources presents a number of unique challenges. First, queries over bioinformatics data sources are often complex associative queries over multiple Web documents. Most associations are defined by string matching of textual fragments in two documents. Second, most of the queries required by Genomics researchers involve complex data extraction, and sophisticated workflows that implement the complex associative access. Third but not the least, complex Genomics-specific queries are often reused many times by Genomics researchers, either directly or through some refinements, and are considered as a part of the research results by Genomics researchers. In this short article we present a list of challenging issues in supporting effective querying over bioinformatics data sources and illustrate them through a selection of representative search scenarios provided by biologists. We end the article with a discussion on how the state-of-art research and technological development in Semantic Web, Ontology, Internet Data Management, and Internet Computing Systems can help addressing these issues.