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.
journal_name
BMC Genomicsjournal_title
BMC genomicsauthors
Llorens F,Hummel M,Pastor X,Ferrer A,Pluvinet R,Vivancos A,Castillo E,Iraola S,Mosquera AM,González E,Lozano J,Ingham M,Dohm JC,Noguera M,Kofler R,del Río JA,Bayés M,Himmelbauer H,Sumoy Ldoi
10.1186/1471-2164-12-326subject
Has Abstractpub_date
2011-06-23 00:00:00pages
326issn
1471-2164pii
1471-2164-12-326journal_volume
12pub_type
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