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Nucleic Acids Research Advance Access originally published online on June 19, 2009
Nucleic Acids Research 2009 37(15):5057-5070; doi:10.1093/nar/gkp520
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Nucleic Acids Research, 2009, Vol. 37, No. 15 5057-5070
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genomics

A computational procedure to identify significant overlap of differentially expressed and genomic imbalanced regions in cancer datasets{dagger}

Silvio Bicciato1,*, Roberta Spinelli2, Mattia Zampieri3, Eleonora Mangano4, Francesco Ferrari5, Luca Beltrame2, Ingrid Cifola2, Clelia Peano2, Aldo Solari6 and Cristina Battaglia4

1Department of Biomedical Sciences, University of Modena and Reggio Emilia, via G. Campi 287, Modena 41100, 2Institute for Biomedical Technologies (ITB), National Research Council (CNR), via Fantoli 16/15, Milano, 3SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, Trieste, 4Department of Biomedical Science and Technologies and CISI, University of Milan, via Fantoli 16/15, Milano, 5Department of Biology, University of Padova, via U. Bassi 58/B and 6Department of Chemical Engineering Processes, University of Padova, via F. Marzolo 9, Padova, Italy

*To whom correspondence should be addressed. Tel: +39-59-2055219; Fax: +39-59-2055410; Email: silvio.bicciato{at}unimore.it

Received February 24, 2009. Accepted June 1, 2009.

The integration of high-throughput genomic data represents an opportunity for deciphering the interplay between structural and functional organization of genomes and for discovering novel biomarkers. However, the development of integrative approaches to complement gene expression (GE) data with other types of gene information, such as copy number (CN) and chromosomal localization, still represents a computational challenge in the genomic arena. This work presents a computational procedure that directly integrates CN and GE profiles at genome-wide level. When applied to DNA/RNA paired data, this approach leads to the identification of Significant Overlaps of Differentially Expressed and Genomic Imbalanced Regions (SODEGIR). This goal is accomplished in three steps. The first step extends to CN a method for detecting regional imbalances in GE. The second part provides the integration of CN and GE data and identifies chromosomal regions with concordantly altered genomic and transcriptional status in a tumor sample. The last step elevates the single-sample analysis to an entire dataset of tumor specimens. When applied to study chromosomal aberrations in a collection of astrocytoma and renal carcinoma samples, the procedure proved to be effective in identifying discrete chromosomal regions of coordinated CN alterations and changes in transcriptional levels.

{dagger}This work is dedicated to the memory of Stefano Ferrari.


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