Investigating the effect of paralogs on microarray gene-set analysis |
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| Authors: | Andre J Faure Cathal Seoighe Nicola J Mulder |
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| Affiliation: | (1) Computational Biology Group, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa;(2) EMBL-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK;(3) School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland |
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| Abstract: | Background In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research. |
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