We have been preparing a new version of Gitools with many improvements, amongst which there is a new IGV search, the use of categorical scales and new [...]
As you may have read in the last post, Günes and Nuria presented the Sample Level Enrichment Analysis (SLEA) as a methodology to analyse the transcription level [...]
Cancer cells often exhibit a change in number of copies of certain genomic regions when compared to normal cells (Copy Number Alterations: CNAs). Some of these CNAs [...]
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. [...]
Good news everyone! Last friday night - just returning from our retreat - we all received an email from Núria announcing the great news that she is [...]
Have you ever heard of SVG? Scalable Vector Graphics (SVG) is an open standard to design images and graphics that can be scaled to any size without [...]
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? Already if it is only 100 GO terms, it is quite hard to get an idea which are the affected compartments. To understand better you have to identify the more general terms that are affected. Here I explain quickly how I solved this problem and share it with you.