Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis
1 Bioinformatics and Genomics Program, Center for Genomic Regulation (CRG) - Universitat Pompeu Fabra (UPF), Barcelona, Spain
2 Molecular and Cellular Neurobiotechnology Group, Institut de Bioenginyeria de Catalunya (IBEC)-Parc Científic de Barcelona, Barcelona, Spain
3 Department of Cell Biology, University of Barcelona (UB), Barcelona, Spain
4 Networked Biomedical Research Center for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
5 Microarray Unit, Genomics Core Facility, Center for Genomic Regulation (CRG) - Universitat Pompeu Fabra (UPF), Barcelona, Spain
6 Genomics Core Facility, Center for Genomic Regulation (CRG) - Universitat Pompeu Fabra (UPF), Barcelona, Spain
7 Genomics Unit, Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Badalona, Spain
8 Ultrasequencing Unit, Genomics Core Facility, Center for Genomic Regulation (CRG) - Universitat Pompeu Fabra (UPF), Barcelona, Spain
9 Cancer Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain
10 Genes and Disease Program, Center for Genomic Regulation (CRG) - Universitat Pompeu Fabra (UPF), Barcelona, Spain
11 Endocrinology Section, Hospital de Sant Joan de Deu, Esplugues de Llobregat, Spain
12 Networked Biomedical Research Center for Diabetes and Associated Metabolic Diseases (CIBERDEM), Barcelona, Spain
13 Servei de Inmunologia, Hospital Clínic i Provincial de Barcelona, Barcelona, Spain
14 Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
15 Networked Biomedical Research Center for Hepatic and Digestive Diseases (CIBERHED), Barcelona, Spain
16 Spanish National center for Genomic Analysis (CNAG), Barcelona, Spain
17 Max Planck Institute for Molecular Genetics, Berlin, Germany
18 IRSI-Caixa, Badalona, Spain
19 Institute for Population Genetics, University of Veterinary Medicine, Vienna, Austria
BMC Genomics 2011, 12:326 doi:10.1186/1471-2164-12-326Published: 23 June 2011
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.
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.
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.