Investigating the effect of paralogs on microarray gene-set analysis

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. Results: We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene http://www.cbio.uct.ac.za/indygene, a web tool that reduces a supplied list of genes so that it includes no pairwise p...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Andre J. Faure, Cathal Seoighe, Nicola J. Mulder
• Keywords: Artificial Intelligence, BMCBI 2011, GSA Methods, Microarray Datasets, Paralogy Relationships