Dataset Information

CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets.

ABSTRACT: The advent of DNA microarray technology and the sequencing of multiple vertebrate genomes has provided a unique opportunity for the integration of comparative genomics with high-throughput gene expression analysis. Here we describe the conserved transcription factor binding site (CONFAC) software that enables the high-throughput identification of conserved transcription factor binding sites (TFBSs) in the regulatory regions of hundreds of genes at a time (http://morenolab.whitehead.emory.edu/cgi-bin/confac/login.pl). The CONFAC software compares non-coding regulatory sequences between human and mouse genomes to enable identification of conserved TFBSs that are significantly enriched in promoters of gene clusters from microarray analyses compared to sets of unchanging control genes using a Mann-Whitney U-test. Analysis of random gene sets demonstrated that using our approach, over 98% of TFBSs had false positive rates below 5%. As a proof-of-principle, we have validated the CONFAC software using gene sets from four separate microarray studies and identified TFBSs known to be functionally important for regulation of each of the four gene sets.

SUBMITTER: Karanam S

PROVIDER: S-EPMC441491 | biostudies-literature | 2004 Jul

REPOSITORIES: biostudies-literature

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CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets.

Karanam Suresh S Moreno Carlos S CS

Nucleic acids research 20040701 Web Server issue

The advent of DNA microarray technology and the sequencing of multiple vertebrate genomes has provided a unique opportunity for the integration of comparative genomics with high-throughput gene expression analysis. Here we describe the conserved transcription factor binding site (CONFAC) software that enables the high-throughput identification of conserved transcription factor binding sites (TFBSs) in the regulatory regions of hundreds of genes at a time (http://morenolab.whitehead.emory.edu/cgi ...[more]

PMID: 15215433

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Project description:BackgroundWith the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft.ResultsThe simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels.ConclusionWe present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the identified genes and pathways may lead to better understanding of CAN at the molecular level.

Dataset Information

CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets.

Publications

CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets.

Similar Datasets

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