As some already may know, Gitools has been integrated with GenomeSpace. Together with the GenomeSpace team we have set up a tutorial that demonstrates the power behind the GenomeSpace approach. The tutorial may be found here and helps the user to extract data from ArrayExpress, normalize it on GenePattern and then load and browse it in Gitools. All these steps are being done without the need to manually download any tool nor data set. You can sign up for GenomeSpace and try it right away.
One of the current challenges of oncogenomics is to elucidate which of the aberrations observed in the cancer genome is involved in the disease and which of them are just bystanders occurring stochastically due to the cell genomic instability, i.e. to distinguish driver events from passengers. This is indeed demanding when analyzing copy number alterations (CNAs), in which very large regions of the DNA (and thus a large number of genes) may be affected. The current approach for dealing with this issue is to assess the recurrence of the alteration across multiple samples, since those alterations occurring more than expected by chance should point out significant events in terms of the disease. However, this exhibits some drawbacks, as the underestimation of low-recurrent drivers, the difficulty of assessing the background model and, finally, that it does not take into account how the alteration may impact the normal behavior of the gene.
A challenge to all cancer genomic studies is to visually explore the generated complex data in a meaningful way to extract relevant knowledge. We have written a review on that topic which has been published last week in Genome Medicine.