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Employing conservation of co-expression to improve functional inference.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholms Bioinformatikcentrum.
2008 (English)In: BMC Syst Biol, ISSN 1752-0509, Vol. 2, p. 81-Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Observing co-expression between genes suggests that they are functionally coupled. Co-expression of orthologous gene pairs across species may improve function prediction beyond the level achieved in a single species. RESULTS: We used orthology between genes of the three different species S. cerevisiae, D. melanogaster, and C. elegans to combine co-expression across two species at a time. This led to increased function prediction accuracy when we incorporated expression data from either of the other two species and even further increased when conservation across both of the two other species was considered at the same time. Employing the conservation across species to incorporate abundant model organism data for the prediction of protein interactions in poorly characterized species constitutes a very powerful annotation method. CONCLUSION: To be able to employ the most suitable co-expression distance measure for our analysis, we evaluated the ability of four popular gene co-expression distance measures to detect biologically relevant interactions between pairs of genes. For the expression datasets employed in our co-expression conservation analysis above, we used the GO and the KEGG PATHWAY databases as gold standards. While the differences between distance measures were small, Spearman correlation showed to give most robust results.

Place, publisher, year, edition, pages
2008. Vol. 2, p. 81-
Keyword [en]
Algorithms, Animals, Base Sequence, Caenorhabditis elegans/genetics, Chromosome Mapping, Conserved Sequence/*genetics, DNA; Complementary/genetics, Drosophila melanogaster/genetics, Evolution; Molecular, Expert Systems, Gene Expression/physiology, Gene Expression Profiling/*methods, Protein Interaction Mapping/methods, Regression Analysis, Saccharomyces cerevisiae/genetics, Sequence Homology; Nucleic Acid, Species Specificity, Weights and Measures
Identifiers
URN: urn:nbn:se:su:diva-17881ISI: 000259952900001PubMedID: 18808668OAI: oai:DiVA.org:su-17881DiVA: diva2:184402
Available from: 2009-01-21 Created: 2009-01-21 Last updated: 2011-01-10Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
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  • de-DE
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