Enhancing data integration with text analysis to find proteins implicated in plant stress response.
High throughput genomic studies can identify large numbers of potential candidate genes, which must be interpreted and filtered by investigators to select the best ones for further analysis. Prioritization is generally based on evidence that supports the role of a gene product in the biological process being investigated. The two most important bodies of information providing such evidence are bioinformatics databases and the scientific literature. In this paper we present an extension to the Ondex data integration framework that uses text mining techniques over Medline abstracts as a method for accessing both these bodies of evidence in a consistent way. In an example use case, we apply our method to create a knowledge base of Arabidopsis proteins implicated in plant stress response and use various scoring metrics to identify key protein-stress associations. In conclusion, we show that the additional text mining features are able to highlight proteins using the scientific literature that would not have been seen using data integration alone. Ondex is an open-source software project and can be downloaded, together with the text mining features described here, from www.ondex.org.
Hassani-Pak
Keywan
K
Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.
Legaie
Roxane
R
Canevet
Catherine
C
van den Berg
Hugo A
HA
Moore
Jonathan D
JD
Rawlings
Christopher J
CJ
D017360:Arabidopsis / Q000378:metabolism*; D029681:Arabidopsis Proteins / Q000378:metabolism*; D057225:Data Mining; D005030:Ethylenes / Q000378:metabolism; D015203:Reproducibility of Results; D013223:Statistics as Topic; D013312:Stress, Physiological
D029681:Arabidopsis Proteins; D005030:Ethylenes; C036216:ethylene