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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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Indygene 'Tool' page. Indygene 'Tool' page showing the form used to submit a gene list for processing.
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Figure 2: Indygene 'Tool' page. Indygene 'Tool' page showing the form used to submit a gene list for processing.

Mentions: The Indygene tool http://www.cbio.uct.ac.za/indygene reduces a supplied list of genes to one without paralogy relationships, where the goal is to proceed with GSA thereafter. The web tool consists of a simple interface allowing the user to submit a list of gene or Affymetrix probe identifiers and select a protein sequence identity threshold for paralogy (see Figure 2). Currently the following taxa are available for selection: Arabidopsis, human, mouse and rat. In addition, the following Affymetrix microarray platforms are available: Arabidopsis ATH1 Genome Array, Arabidopsis Genome Array, Human Genome Focus Array, Human Genome U133 Array Plate Set, Human Genome U133 Plus 2.0 Array, Human Genome U133 Set, Human Genome U133A 2.0 Array, Human Genome U95 Set. The 'Output' page provides links to the reduced gene list and log file. The former contains gene identifiers in their originally submitted format ready for use in conjunction with any preferred GSA tool.


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

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

Indygene 'Tool' page. Indygene 'Tool' page showing the form used to submit a gene list for processing.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Indygene 'Tool' page. Indygene 'Tool' page showing the form used to submit a gene list for processing.
Mentions: The Indygene tool http://www.cbio.uct.ac.za/indygene reduces a supplied list of genes to one without paralogy relationships, where the goal is to proceed with GSA thereafter. The web tool consists of a simple interface allowing the user to submit a list of gene or Affymetrix probe identifiers and select a protein sequence identity threshold for paralogy (see Figure 2). Currently the following taxa are available for selection: Arabidopsis, human, mouse and rat. In addition, the following Affymetrix microarray platforms are available: Arabidopsis ATH1 Genome Array, Arabidopsis Genome Array, Human Genome Focus Array, Human Genome U133 Array Plate Set, Human Genome U133 Plus 2.0 Array, Human Genome U133 Set, Human Genome U133A 2.0 Array, Human Genome U95 Set. The 'Output' page provides links to the reduced gene list and log file. The former contains gene identifiers in their originally submitted format ready for use in conjunction with any preferred GSA tool.

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