title: 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.
authors.affiliation: Center for Genomic Regulation (CRG)-Universitat Pompeu Fabra (UPF), Barcelona, Spain.
authors.forename: Franc
authors.initials: F
authors.lastname: Llorens
authors.forename: Manuela
authors.initials: M
authors.lastname: Hummel
authors.forename: Xavier
authors.initials: X
authors.lastname: Pastor
authors.forename: Anna
authors.initials: A
authors.lastname: Ferrer
authors.forename: Raquel
authors.initials: R
authors.lastname: Pluvinet
authors.forename: Ana
authors.initials: A
authors.lastname: Vivancos
authors.forename: Ester
authors.initials: E
authors.lastname: Castillo
authors.forename: Susana
authors.initials: S
authors.lastname: Iraola
authors.forename: Ana M
authors.initials: AM
authors.lastname: Mosquera
authors.forename: Eva
authors.initials: E
authors.lastname: González
authors.forename: Juanjo
authors.initials: J
authors.lastname: Lozano
authors.forename: Matthew
authors.initials: M
authors.lastname: Ingham
authors.forename: Juliane C
authors.initials: JC
authors.lastname: Dohm
authors.forename: Marc
authors.initials: M
authors.lastname: Noguera
authors.forename: Robert
authors.initials: R
authors.lastname: Kofler
authors.forename: Jose Antonio
authors.initials: JA
authors.lastname: del Río
authors.forename: Mònica
authors.initials: M
authors.lastname: Bayés
authors.forename: Heinz
authors.initials: H
authors.lastname: Himmelbauer
authors.forename: Lauro
authors.initials: L
authors.lastname: Sumoy
mesh_terms: D004815:Epidermal Growth Factor / Q000494:pharmacology*; D020869:Gene Expression Profiling; D006367:HeLa Cells; D006801:Humans; D015201:Meta-Analysis as Topic; D053858:Metabolic Networks and Pathways / Q000235:genetics; D008668:Metallothionein / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D015398:Signal Transduction; D012984:Software
chemical_list: D004815:Epidermal Growth Factor; D008668:Metallothionein