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).
Finding correlated genes on TCGA expression data.
We have published a new section in IntOGen in collaboration with Hautaniemi Lab, where you can do a gene correlation with the gene expression results of TCGA (The Cancer Genome Atlas) for a given subset of patients that share some specific clinical annotations. Then you will be able to browse your correlations next to global gene expression, SNP survival and DNA methylation analyses. The data analyzed so far includes 4 tumor types from TCGA: glioblastoma multiforme (GBM; 507 samples), ovarian serous adenocarcinoma (OV; 546 samples), breast invasive carcinoma (BRCA; 525 samples) and colon adenocarcinoma (COAD; 161 samples).
IntOGen Biomart portal
The DATABASE Oxford Journal has published a new volume in September dedicated to Biomart related databases and tools.
We have had a Biomart portal for IntOGen data since 2010 and we were invited to write a manuscript describing it. Read the rest of this entry »
Version 3 of IntOGen!
We have worked hard during the last months to release the new version of IntOGen, version 03, which we have published today. We are very proud of this new release, as it includes some major improvements. Read the rest of this entry »
Browsing ICGC data in IntOGen
When we created IntOGen we had the motivation to convert it into a discovery tool for cancer researchers and a resource that integrates multidimensional OncoGenomics Data. We have now made a new important step in IntOGen project: we have included all somatic mutations detected by the International Cancer Genome Consortium (ICGC) projects. Read the rest of this entry »
Understanding the hierarchy of your GO Terms subset
The other day I downloaded the cancer-affected Gene Ontology (GO) terms from IntOGen for up- and down-regulation via it’s Biomart interface for a few tissues. Since was only interested in the GO Cell Compartment terms, so I directly added a filter file containing all the GO CC Terms as a filter for the Biomart export.
So then… what do you do when you have a list of GO terms? Read the rest of this entry »
Basic and intuitive analysis of microarray datasets using Gitools (Part 2)
In this series of posts I am showing how expression data can be analyzed using Gitools. In a previous post I explained how to do pathway enrichment analysis. Here I explain how to identify genes significantly up-regulated in a cancer dataset using Oncodrive. Read the rest of this entry »
Basic and intuitive analysis of microarray datasets using Gitools (Part 1)
After preparing some tutorials for our software, I thought it would be useful to show how basic analyses on microarray data can be carried out using Gitools.
For that, first I needed to find a nice dataset that would serve as an example in Gitools. Read the rest of this entry »
Three questions you can answer with IntOGen
1. Do you have a favorite gene? Would you like to know if it is altered in cancer samples in any way (e.g. mutated, amplified, downregulated)? Read the rest of this entry »


