Dataset Information

A cross-comparison of technologies for the detection of microRNAs from FFPE samples of hepatoblastoma patients [microarray]

ABSTRACT: This work highlights similarities and differences between three platforms (next-generation sequencing, microarray and NanoString) for detecting miRNAs and compares their strengths and weaknesses.

ORGANISM(S): synthetic construct Homo sapiens

PROVIDER: GSE62011 | GEO | 2015/05/03

SECONDARY ACCESSION(S): PRJNA262947

REPOSITORIES: GEO

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Project description:Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3â??-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially-available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. METHODS: Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary Coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. RESULTS: Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with â??steroid biosynthesisâ?? consistently identified as the most down-regulated metabolic pathway by probucol treatment. CONCLUSIONS: This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly-expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges. 4 samples are analyzed on each of 4 different Mouse Microarray Expression platforms (Affymetrix Mo430 2.0, Affymetrix Gene, Affymetrix Exon 1.0 ST, and Illumina Whole Genome 6).

Project description:Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3’-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially-available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. METHODS: Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary Coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. RESULTS: Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with ‘steroid biosynthesis’ consistently identified as the most down-regulated metabolic pathway by probucol treatment. CONCLUSIONS: This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly-expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.

Dataset Information

A cross-comparison of technologies for the detection of microRNAs from FFPE samples of hepatoblastoma patients [microarray]

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