Mutual exclusion statistics and data events in Gitools

We’re pleased to announce another incremental release of Gitools, version 2.2. Amongst the many improvements (listed at the bottom of this post) we’d like to highlight the effort that we put into improving performance, specifically with genomic data: mutual exclusion and co-occurrence statistics coupled with a new feature called “data events” – which helps to get a quick grasp of the data.

Low expression events ordered by mutual exclusion

Low expression data events events ordered by mutual exclusion

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Visualizing mutually exclusive alteration patterns in cancer with Gitools

On the quest to identify cancer driver genes, it has been observed that driver alterations that affect a pathway tend to be altered in a mutually exclusive manner. As far as we know this was first observed by Thomas et al., Nat Genet 2007, however from our experience this type of pattern can be observed in data from almost all cancer genomic projects. The rationale behind that observation is that once a gene involved in a particular critical pathway is altered, a second alteration affecting the same pathway does not confer a further selective advantage to the cancer cell. The concept of mutually exclusive alteration patterns has recently been exploited to identify cancer drivers (Ciriello et al, Genome Research 2011 and Vandin et al., Genome Research 2012).

Mutually exclusive sorting of p53-signalling pathway upstream genes

The heatmap in the left shows copy number alterations of TCGA Glioblastoma project in the KEGG TP53-signalling pathway. If sorted properly we can observe that the upstream genes show a mutual-exclusive alteration pattern, but not PTEN and CDK4. Loss in blue, gain in red.

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