Cancer Informatics

Ganesh Shankar¹, Michael R. Rossi¹, Devin E. McQuaid¹, Jeffrey M. Conroy¹, Daniel G. Gaile², John K. Cowell¹, Norma J. Nowak¹, Ping Liang<sup>1

1</sup>Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263 USA; ²Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214 USA.

Abstract: Array-Comparative Genomic Hybridization (aCGH) is a powerful high throughput technology for detecting chromosomal copy number aberrations (CNAs) in cancer, aiming at identifying related critical genes from the affected genomic regions. However, advancing from a dataset with thousands of tabular lines to a few candidate genes can be an onerous and time-consuming process. To expedite the aCGH data analysis process, we have developed a user-friendly aCGH data viewer (aCGHViewer) as a conduit between the aCGH data tables and a genome browser. The data from a given aCGH analysis are displayed in a genomic view comprised of individual chromosome panels which can be rapidly scanned for interesting features. A chromosome panel containing a feature of interest can be selected to launch a detail window for that single chromosome. Selecting a data point of interest in the detail window launches a query to the UCSC or NCBI genome browser to allow the user to explore the gene content in the chromosomal region. Additionally, aCGHViewer can display aCGH and expression array data concurrently to visually correlate the two. aCGHViewer is a stand alone Java visualization application that should be used in conjunction with separate statistical programs. It operates on all major computer platforms and is freely available at http://falcon.roswellpark.org/aCGHview/.

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