Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78 -- 0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62 -- 0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of.200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment

We designed and successfully implemented the use of in situ-synthesized 45-mer oligonucleotide DNA microarrays (XeoChips) for genome-wide expression profiling of Burkholderia xenovorans LB400, which is among the best aerobic polychlorinated biphenyl degraders known so far. We conducted differential gene expression profiling during exponential growth on succinate, benzoate, and biphenyl as sole carbon sources and investigated the transcriptome of early-stationary-phase cells grown on biphenyl. Based on these experiments, we outlined metabolic pathways and summarized other cellular functions in the organism relevant for biphenyl and benzoate degradation. All genes previously identified as being directly involved in biphenyl degradation were up-regulated when cells were grown on biphenyl compared to expression in succinate-grown cells. For benzoate degradation, however, genes for an aerobic coenzyme A activation pathway were upregulated in biphenyl-grown cells, while the pathway for benzoate degradation via hydroxylation was upregulated in benzoate-grown cells. The early-stationary-phase biphenyl-grown cells showed similar expression of biphenyl pathway genes, but a surprising up-regulation of C 1 metabolic pathway genes was observed. The microarray results were validated by quantitative reverse transcription PCR with a subset of genes of interest. The XeoChips showed a chip-to-chip variation of 13.9%, compared to the 21.6 % variation for spotted oligonucleotide

The challenge with todays microarray experiments is to infer biological conclusions from them. There are two crucial difficulties to be surmounted in this challenge:(1) A lack of suitable biological repository that can be easily integrated into computational algorithms. (2) Contemporary algorithms used to analyze microarray data are unable to draw consistent biological results from diverse datasets of the same disease. To deal with the first difficulty, we believe a core database that unifies available biological repositories is important. Towards this end, we create a unified biological database from three popular biological repositories (KEGG, Ingenuity and Wikipathways). This database provides computer scientists the flexibility of easily integrating biological information using simple API calls or SQL queries. To deal with the second difficulty of deriving consistent biological results from the experiments, we first conceptualize the notion of “subnetworks”, which refers to a

Abstract not found

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78–0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62–0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays. D 2004 Elsevier Inc. All rights reserved.

A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments

short oligonucleotide microarray data

short oligonucleotide microarray data

Enhanced identification and biological validation of differential gene expression via Illumina whole-genome expression arrays through the use of the model-based background correction methodology