Dataset Information

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

ABSTRACT: BACKGROUND: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. RESULTS: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. CONCLUSIONS: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

SUBMITTER: Llorens F

PROVIDER: S-EPMC3141672 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

Llorens Franc F Hummel Manuela M Pastor Xavier X Ferrer Anna A Pluvinet Raquel R Vivancos Ana A Castillo Ester E Iraola Susana S Mosquera Ana M AM González Eva E Lozano Juanjo J Ingham Matthew M Dohm Juliane C JC Noguera Marc M Kofler Robert R del Río Jose Antonio JA Bayés Mònica M Himmelbauer Heinz H Sumoy Lauro L

BMC genomics 20110623

<h4>Background</h4>Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.<h4>Results</h4>By a ...[more]

PMID: 21699700

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Project description:Epidermal growth factor (EGF) is a key regulatory growth factor activating a myriad of processes affecting cell proliferation and survival that are relevant to normal development and disease. Here we have used a combined approach to study the EGF dependent transcriptome of HeLa cells. We obtained mRNA expression profiles using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, Febit, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer I (GA-I). By applying a procedure for cross-platform data meta-analysis based on rank product and global ancova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We used this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we found a whole new set of genes previously unrelated to the currently accepted EGF associated cellular functions, among which are metallothionein genes. We propose the use of global genomic cross-validation to generate more reliable datasets derived from high content technologies (microarrays or deep sequencing). This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. Keywords: treated vs. untreated comparison, time course Time course experiment comparing HeLa gene expression in response to EGF analyzed on different microarray platforms (Agilent, IMPPC, Illumina, and Operon) and by digital gene expression using short read high throughput tag sequencing. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6, and 24 h and harvested for RNA extraction. Technical dye swap duplicates were performed for each of the three biological replicates in both time points. Comparative genomic hybridization of HeLa cell genomic DNA versus poooled genomic DNA from blood obtained from human females conducted on commercial oligonucleotide microarrays (Human Genome CGH Microarray Kit 244A, Agilent Technologies) in order to assess DNA dosage dependence of gene expression levels and response to EGF. Digital gene expression using short read high throughput tag sequencing data submitted to NCBI's SRA

Dataset Information

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

Publications

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

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