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Investigating the effect of paralogs on microarray gene-set analysis.

Faure AJ, Seoighe C, Mulder NJ - BMC Bioinformatics (2011)

Bottom Line: 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.The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses.This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biology Group, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa. andrefau@ebi.ac.uk

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.

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 paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs.

Conclusions: The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

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Paralog expression correlation. Mean expression correlation (Spearman's ρ) of gene paralogs in Arabidopsis at various protein sequence identity levels where %ID > 20. Error bars indicate the standard error of the estimated mean values. The values in parentheses indicate the number of unique pairwise gene comparisons in each case.
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Figure 1: Paralog expression correlation. Mean expression correlation (Spearman's ρ) of gene paralogs in Arabidopsis at various protein sequence identity levels where %ID > 20. Error bars indicate the standard error of the estimated mean values. The values in parentheses indicate the number of unique pairwise gene comparisons in each case.

Mentions: After attributing gene names to all protein pairs and removing redundant gene matches and those without associated gene name information, 677473 gene pairs remained. We investigated the coexpression of paralogs using a large Arabidopsis microarray gene expression dataset from the Nottingham Arabidopsis Stock Centre's (NASC) AffyWatch service [31]. Gene expression values across multiple experiments were used to calculate correlation values for each paralog pair. We were able to estimate Spearman's rank correlation coefficient for 409944 gene pairs (not all paralogs found were represented on the microarray platform used) and Figure 1 shows the mean expression correlation for paralogs at different levels of protein identity. Gene expression correlation of paralogs showed a clear tendency to increase with increasing protein sequence similarity. On average, paralogs with 90-100% protein sequence identity have a strong correlation () in their gene expression patterns.


Investigating the effect of paralogs on microarray gene-set analysis.

Faure AJ, Seoighe C, Mulder NJ - BMC Bioinformatics (2011)

Paralog expression correlation. Mean expression correlation (Spearman's ρ) of gene paralogs in Arabidopsis at various protein sequence identity levels where %ID > 20. Error bars indicate the standard error of the estimated mean values. The values in parentheses indicate the number of unique pairwise gene comparisons in each case.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037853&req=5

Figure 1: Paralog expression correlation. Mean expression correlation (Spearman's ρ) of gene paralogs in Arabidopsis at various protein sequence identity levels where %ID > 20. Error bars indicate the standard error of the estimated mean values. The values in parentheses indicate the number of unique pairwise gene comparisons in each case.
Mentions: After attributing gene names to all protein pairs and removing redundant gene matches and those without associated gene name information, 677473 gene pairs remained. We investigated the coexpression of paralogs using a large Arabidopsis microarray gene expression dataset from the Nottingham Arabidopsis Stock Centre's (NASC) AffyWatch service [31]. Gene expression values across multiple experiments were used to calculate correlation values for each paralog pair. We were able to estimate Spearman's rank correlation coefficient for 409944 gene pairs (not all paralogs found were represented on the microarray platform used) and Figure 1 shows the mean expression correlation for paralogs at different levels of protein identity. Gene expression correlation of paralogs showed a clear tendency to increase with increasing protein sequence similarity. On average, paralogs with 90-100% protein sequence identity have a strong correlation () in their gene expression patterns.

Bottom Line: 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.The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses.This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biology Group, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa. andrefau@ebi.ac.uk

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.

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 paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs.

Conclusions: The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

Show MeSH