Abstract
A software resource for the analysis of cell cycle related molecular networks.
Highlights
Molecular biology has spent the past two decades cataloguing genes, expression levels, proteins, molecular interactions and more
Bio-ontologies While it is easy to compare nucleic acid or polypeptide sequences from different bioinformatics resources, the biological knowledge contained in these resources is very difficult to compare as it is represented in a wide variety of lexical forms [13,14,15], and there are no tools that facilitate an easy comparison and integration of knowledge in this form
Bio-ontologies and their presentations have been made accessible through existing software tools, or web-based tools such as BioPortal [52], which can be used to create new terms and relationships and to explore and analyze these ontologies)
Summary
Bio-ontologies and their presentations have been made accessible through existing software tools (such as OBO-Edit [50], Protégé [51]), or web-based tools such as BioPortal [52], which can be used to create new terms and relationships and to explore and analyze these ontologies). The following SPARQL query on the A. thaliana graph allows users to infer a putative location for proteins with no documented cellular locations The assumption behind such a query is that two proteins that participate in the same interaction are likely to share the same cellular location, for example, the 'nucleus' (CCO_C0000252): GRAPH { ?unique_id rdfs:label ?name. The query returns 48 proteins (for example, DMC1_ARATH, SEM12_ARATH) having an interaction with a documented nuclear protein, meaning their own cellular location is likely to include 'nucleus' at some point These results and, more generally, any answer to a query on CCO reflects the information in the original sources, but their integration enables the construction of new hypotheses. We envision that by improving both content and semantics, the utility of CCO can be considerably increased
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