Dataset Information

Detection of transcriptional difference of porcine imprinted genes using different microarray platforms.

ABSTRACT:

Background

Presently, multiple options exist for conducting gene expression profiling studies in swine. In order to determine the performance of some of the existing microarrays, Affymetrix Porcine, Affymetrix Human U133+2.0, and the U.S. Pig Genome Coordination Program spotted glass oligonucleotide microarrays were compared for their reproducibility, coverage, platform independent and dependent sensitivity using fibroblast cell lines derived from control and parthenogenic porcine embryos.

Results

Array group correlations between technical replicates demonstrated comparable reproducibility in both Affymetrix arrays. Glass oligonucleotide arrays showed greater variability and, in addition, approximately 10% of probes had to be discarded due to slide printing defects. Probe level analysis of Affymetrix Human arrays revealed significant variability within probe sets due to the effects of cross-species hybridization. Affymetrix Porcine arrays identified the greatest number of differentially expressed genes amongst probes common to all arrays, a measure of platform sensitivity. Affymetrix Porcine arrays also identified the greatest number of differentially expressed known imprinted genes using all probes on each array, an ad hoc measure of realistic performance for this particular experiment.

Conclusion

We conclude that of the platforms currently available and tested, the Affymetrix Porcine array is the most sensitive and reproducible microarray for swine genomic studies.

SUBMITTER: Tsai S

PROVIDER: S-EPMC1769376 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

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Publications

Detection of transcriptional difference of porcine imprinted genes using different microarray platforms.

Tsai Shengdar S Mir Bashir B Martin Amy C AC Estrada Jose L JL Bischoff Steve R SR Hsieh Wen-ping WP Cassady Joseph P JP Freking Bradley A BA Nonneman Dan J DJ Rohrer Gary A GA Piedrahita Jorge A JA

BMC genomics 20061228

<h4>Background</h4>Presently, multiple options exist for conducting gene expression profiling studies in swine. In order to determine the performance of some of the existing microarrays, Affymetrix Porcine, Affymetrix Human U133+2.0, and the U.S. Pig Genome Coordination Program spotted glass oligonucleotide microarrays were compared for their reproducibility, coverage, platform independent and dependent sensitivity using fibroblast cell lines derived from control and parthenogenic porcine embryo ...[more]

PMID: 17194308

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Project description:BACKGROUND: Microarrays have become a routine tool to address diverse biological questions. Therefore, different types and generations of microarrays have been produced by several manufacturers over time. Likewise, the diversity of raw data deposited in public databases such as NCBI GEO or EBI ArrayExpress has grown enormously.This has resulted in databases currently containing several hundred thousand microarray samples clustered by different species, manufacturers and chip generations. While one of the original goals of these databases was to make the data available to other researchers for independent analysis and, where appropriate, integration with their own data, current software implementations could not provide that feature.Only those data sets generated on the same chip platform can be readily combined and even here there are batch effects to be taken care of. A straightforward approach to deal with multiple chip types and batch effects has been missing.The software presented here was designed to solve both of these problems in a convenient and user friendly way. RESULTS: The virtualArray software package can combine raw data sets using almost any chip types based on current annotations from NCBI GEO or Bioconductor. After establishing congruent annotations for the raw data, virtualArray can then directly employ one of seven implemented methods to adjust for batch effects in the data resulting from differences between the chip types used. Both steps can be tuned to the preferences of the user. When the run is finished, the whole dataset is presented as a conventional Bioconductor "ExpressionSet" object, which can be used as input to other Bioconductor packages. CONCLUSIONS: Using this software package, researchers can easily integrate their own microarray data with data from public repositories or other sources that are based on different microarray chip types. Using the default approach a robust and up-to-date batch effect correction technique is applied to the data.

Project description:BACKGROUND: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. RESULTS: Gene expression profiles in the hippocampus of five wild-type and five transgenic deltaC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. CONCLUSION: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression.

Dataset Information

Detection of transcriptional difference of porcine imprinted genes using different microarray platforms.

Background

Results

Conclusion

Publications

Detection of transcriptional difference of porcine imprinted genes using different microarray platforms.

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