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

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Dohm,  J. C.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Himmelbauer,  H.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Llorens, F., Hummel, M., Pastor, X., Ferrer, A., Pluvinet, R., Vivancos, A., et al. (2011). Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis. BMC Genomics, 12, 326. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21699700 http://www.biomedcentral.com/1471-2164/12/326 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141672/pdf/1471-2164-12-326.pdf?tool=pmcentrez.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-78D0-9
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.