Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis
<p>Abstract</p> <p>Background</p> <p>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 depende...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2011-06-01
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| Series: | BMC Genomics |
| Online Access: | http://www.biomedcentral.com/1471-2164/12/326 |
| _version_ | 1818473719924785152 |
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| author | Iraola Susana Castillo Ester Vivancos Ana Pluvinet Raquel Ferrer Anna Pastor Xavier Hummel Manuela Llorens Franc Mosquera Ana M González Eva Lozano Juanjo Ingham Matthew Dohm Juliane C Noguera Marc Kofler Robert del Río Jose Bayés Mònica Himmelbauer Heinz Sumoy Lauro |
| author_facet | Iraola Susana Castillo Ester Vivancos Ana Pluvinet Raquel Ferrer Anna Pastor Xavier Hummel Manuela Llorens Franc Mosquera Ana M González Eva Lozano Juanjo Ingham Matthew Dohm Juliane C Noguera Marc Kofler Robert del Río Jose Bayés Mònica Himmelbauer Heinz Sumoy Lauro |
| author_sort | Iraola Susana |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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 <it>in silico </it>functional inference analyses based on high content data.</p> |
| first_indexed | 2024-04-14T04:26:26Z |
| format | Article |
| id | doaj.art-8b142d35fe76421fb29a35d2e6c3b63d |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-14T04:26:26Z |
| publishDate | 2011-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-8b142d35fe76421fb29a35d2e6c3b63d2022-12-22T02:12:17ZengBMCBMC Genomics1471-21642011-06-0112132610.1186/1471-2164-12-326Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysisIraola SusanaCastillo EsterVivancos AnaPluvinet RaquelFerrer AnnaPastor XavierHummel ManuelaLlorens FrancMosquera Ana MGonzález EvaLozano JuanjoIngham MatthewDohm Juliane CNoguera MarcKofler Robertdel Río JoseBayés MònicaHimmelbauer HeinzSumoy Lauro<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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 <it>in silico </it>functional inference analyses based on high content data.</p>http://www.biomedcentral.com/1471-2164/12/326 |
| spellingShingle | Iraola Susana Castillo Ester Vivancos Ana Pluvinet Raquel Ferrer Anna Pastor Xavier Hummel Manuela Llorens Franc Mosquera Ana M González Eva Lozano Juanjo Ingham Matthew Dohm Juliane C Noguera Marc Kofler Robert del Río Jose Bayés Mònica Himmelbauer Heinz Sumoy Lauro Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis BMC Genomics |
| title | Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis |
| title_full | Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis |
| title_fullStr | Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis |
| title_full_unstemmed | Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis |
| title_short | Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis |
| title_sort | multiple platform assessment of the egf dependent transcriptome by microarray and deep tag sequencing analysis |
| url | http://www.biomedcentral.com/1471-2164/12/326 |
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