Francisella tularensis novicida proteomic and transcriptomic data integration and annotation based on semantic web technologies

• Published in: BMC bioinformatics 2009-10, Vol.10 Suppl 10 (Suppl 10), p.S3-S3, Article S3
• Main Authors: Anwar, Nadia, Hunt, Ela
• Format: Article
• Language: English
• Subjects: Computational Biology - methods; Databases, Protein; Francisella tularensis; Francisella tularensis - genetics; Francisella tularensis - metabolism; Gene Expression Profiling; Internet; Proteomics - methods
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-10-S10-S3

This paper summarises the lessons and experiences gained from a case study of the application of semantic web technologies to the integration of data from the bacterial species Francisella tularensis novicida (Fn). Fn data sources are disparate and heterogeneous, as multiple laboratories across the world, using multiple technologies, perform experiments to understand the mechanism of virulence. It is hard to integrate these data sources in a flexible manner that allows new experimental data to be added and compared when required. Public domain data sources were combined in RDF. Using this connected graph of database cross references, we extended the annotations of an experimental data set by superimposing onto it the annotation graph. Identifiers used in the experimental data automatically resolved and the data acquired annotations in the rest of the RDF graph. This happened without the expensive manual annotation that would normally be required to produce these links. This graph of resolved identifiers was then used to combine two experimental data sets, a proteomics experiment and a transcriptomic experiment studying the mechanism of virulence through the comparison of wildtype Fn with an avirulent mutant strain. We produced a graph of Fn cross references which enabled the combination of two experimental datasets. Through combination of these data we are able to perform queries that compare the results of the two experiments. We found that data are easily combined in RDF and that experimental results are easily compared when the data are integrated. We conclude that semantic data integration offers a convenient, simple and flexible solution to the integration of published and unpublished experimental data.