Merging OpenLifeData with SADI services using Galaxy and Docker

2015 ◽  
Author(s):  
Mikel Egañna Aranguren
Keyword(s):  
Bioinformatics Analysis ◽  
Life Sciences ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Semantic Data ◽  
Worked Example ◽  
Data Store ◽  
The Galaxy ◽  
Galaxy Server ◽  
Analysis Platform

ABSTRACTSemantic Web technologies have been widely applied in Life Sciences, for example by data providers like OpenLifeData and Web Services frameworks like SADI. The recent OpenLifeData2SADI project offers access to the OpenLifeData data store through SADI services. This paper shows how to merge data from OpenLifeData with other extant SADI services in the Galaxy bioinformatics analysis platform, making semantic data amenable to complex analyses, as a worked example demonstrates. The whole setting is reproducible through a Docker image that includes a pre-configured Galaxy server with data and workflows that constitute the worked example.

scholarly journals Semantic Web technologies for the big data in life sciences

2014 ◽  
Vol 8 (4) ◽  
pp. 192-201 ◽  
Author(s):  
Hongyan Wu ◽  
Atsuko Yamaguchi
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Life Sciences ◽  
Semantic Web Technologies ◽  
Web Technologies

Semantic Web Technologies for Business Intelligence

2011 ◽  
pp. 310-339 ◽  
Author(s):  
Rafael Berlanga ◽  
Oscar Romero ◽  
Alkis Simitsis ◽  
Victoria Nebot ◽  
Torben Bach Pedersen ◽  
...  
Keyword(s):  
Semantic Web ◽  
Business Intelligence ◽  
Full Potential ◽  
Semantic Web Technologies ◽  
Data Annotation ◽  
Web Technologies ◽  
Critical Knowledge ◽  
Semantic Data ◽  
External Data ◽  
The Relationship

This chapter describes the convergence of two of the most influential technologies in the last decade, namely business intelligence (BI) and the Semantic Web (SW). Business intelligence is used by almost any enterprise to derive important business-critical knowledge from both internal and (increasingly) external data. When using external data, most often found on the Web, the most important issue is knowing the precise semantics of the data. Without this, the results cannot be trusted. Here, Semantic Web technologies come to the rescue, as they allow semantics ranging from very simple to very complex to be specified for any web-available resource. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The chapter first presents a motivating running example, followed by an introduction to the relevant SW foundation concepts. The chapter then goes on to survey the use of SW technologies for data integration, including semantic data annotation and semantics-aware extract, transform, and load processes (ETL). Next, the chapter describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms, and the use of advanced SW reasoning functionality on MD models. Finally, the chapter describes in detail a number of directions for future research, including SW support for intelligent BI querying, using SW technologies for providing context to data warehouses, and scalability issues. The overall conclusion is that SW technologies are very relevant for the future of BI, but that several new developments are needed to reach the full potential.

scholarly journals Enabling Interoperability in the Internet of Things

2017 ◽  
Vol 13 (1) ◽  
pp. 147-167 ◽  
Author(s):  
Alfredo D'Elia ◽  
Fabio Viola ◽  
Luca Roffia ◽  
Paolo Azzoni ◽  
Tullio Salmon Cinotti
Keyword(s):  
Internet Of Things ◽  
Semantic Web ◽  
Smart Cities ◽  
The Internet ◽  
Semantic Web Technologies ◽  
Public Authorities ◽  
Web Technologies ◽  
Data Store ◽  
Rdf Graphs ◽  
The Internet Of Things

Semantic Web technologies act as an interoperability glue among different formats, protocols and platforms, providing a uniform vision of heterogeneous devices and services in the Internet of Things (IoT). Semantic Web technologies can be applied to a broad range of application contexts (i.e., industrial automation, automotive, health care, defense, finance, smart cities) involving heterogeneous actors (i.e., end users, communities, public authorities, enterprises). Smart-M3 is a semantic publish-subscribe software architecture conceived to merge the Semantic Web and the IoT domains. It is based on a core component (SIB, Semantic Information Broker) where data is stored as RDF graphs, and software agents using SPARQL to update, retrieve and subscribe to changes in the data store. This article describes a OSGi SIB implementation extended with a new persistent SPARQL update primitive. The OSGi SIB performance has been evaluated and compared with the reference C implementation. Eventually, a first porting on Android is presented.

scholarly journals OBO and OWL: Leveraging Semantic Web Technologies for the Life Sciences

2007 ◽  
pp. 169-182 ◽  
Author(s):  
Christine Golbreich ◽  
Matthew Horridge ◽  
Ian Horrocks ◽  
Boris Motik ◽  
Rob Shearer
Keyword(s):  
Semantic Web ◽  
Life Sciences ◽  
Semantic Web Technologies ◽  
Web Technologies

Semantic Web Technologies for e-Learning: Models and Implementation

Informatica ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 221-240 ◽  
Author(s):  
Valentina Dagienė ◽  
Daina Gudonienė ◽  
Renata Burbaitė
Keyword(s):  
Semantic Web ◽  
Learning Models ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
E Learning

scholarly journals Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB

Metabolites ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 48
Author(s):  
Marc Feuermann ◽  
Emmanuel Boutet ◽  
Anne Morgat ◽  
Kristian Axelsen ◽  
Parit Bansal ◽  
...  
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Semantic Web Technologies ◽  
Knowledge Resources ◽  
Web Technologies ◽  
Chemical Structures ◽  
Biological Interest ◽  
Catalyzed Reactions ◽  
Enzyme Catalyzed Reactions ◽  
Practical Demonstration

The UniProt Knowledgebase UniProtKB is a comprehensive, high-quality, and freely accessible resource of protein sequences and functional annotation that covers genomes and proteomes from tens of thousands of taxa, including a broad range of plants and microorganisms producing natural products of medical, nutritional, and agronomical interest. Here we describe work that enhances the utility of UniProtKB as a support for both the study of natural products and for their discovery. The foundation of this work is an improved representation of natural product metabolism in UniProtKB using Rhea, an expert-curated knowledgebase of biochemical reactions, that is built on the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Knowledge of natural products and precursors is captured in ChEBI, enzyme-catalyzed reactions in Rhea, and enzymes in UniProtKB/Swiss-Prot, thereby linking chemical structure data directly to protein knowledge. We provide a practical demonstration of how users can search UniProtKB for protein knowledge relevant to natural products through interactive or programmatic queries using metabolite names and synonyms, chemical identifiers, chemical classes, and chemical structures and show how to federate UniProtKB with other data and knowledge resources and tools using semantic web technologies such as RDF and SPARQL. All UniProtKB data are freely available for download in a broad range of formats for users to further mine or exploit as an annotation source, to enrich other natural product datasets and databases.

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