The SIB Swiss Institute of Bioinformatics Semantic Web of data

Author:

,Altenhoff AdrianORCID,Bairoch AmosORCID,Bansal ParitORCID,Baratin DelphineORCID,Bastian FredericORCID,Bolleman* JervenORCID,Bridge AlanORCID,Burdet FrédéricORCID,Crameri KatrinORCID,Dauvillier JérômeORCID,Dessimoz ChristopheORCID,Gehant SebastienORCID,Glover NatashaORCID,Gnodtke Kristin,Hayes CatherineORCID,Ibberson MarkORCID,Kriventseva EvgeniaORCID,Kuznetsov DmitryORCID,Frédérique LisacekORCID,Mehl FlorenceORCID,Mendes de Farias* TarcisioORCID,Michel Pierre-AndréORCID,Moretti SébastienORCID,Morgat AnneORCID,Österle SabineORCID,Pagni MarcoORCID,Redaschi NicoleORCID,Robinson-Rechavi MarcORCID,Samarasinghe KasunORCID,Sima Ana-ClaudiaORCID,Szklarczyk DamianORCID,Topalov OrlinORCID,Touré VasundraORCID,Unni DeepakORCID,von Mering ChristianORCID,Wollbrett JulienORCID,Zahn-Zabal* MoniqueORCID,Zdobnov EvgenyORCID

Abstract

Abstract The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss/) is a federation of bioinformatics research and service groups. The international life science community in academia and industry has been accessing the freely available databases provided by SIB since its inception in 1998. In this paper we present the 11 databases which currently offer semantically enriched data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable), as well as the Swiss Personalized Health Network initiative (SPHN) which also employs this enrichment. The semantic enrichment facilitates the manipulation of large data sets from public databases and private data sets. Examples are provided to illustrate that the data from the SIB databases can not only be queried using precise criteria individually, but also across multiple databases, including a variety of non-SIB databases. Data manipulation, be it exploration, extraction, annotation, combination, and publication, is possible using the SPARQL query language. Providing documentation, tutorials and sample queries makes it easier to navigate this web of semantic data. Through this paper, the reader will discover how the existing SIB knowledge graphs can be leveraged to tackle the complex biological or clinical questions that are being addressed today.

Funder

SIB Swiss Institute of Bioinformatics

Publisher

Oxford University Press (OUP)

Subject

Genetics

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