One of the most amazing groups whose work I have recently explored, is based in the rapidly- growing young UPF university in Barcelona. The Biomedical Genomics Group applies its high computational expertise to cancer research, focusing on the identification of those mutations that are actually involved in determining the tumor phenotype, the so- called driver mutations. The tool I share with you today is aimed at the identification of driver mutations using a clustering approach. The idea is quite simple: since gain of function mutations in cancer use to cluster in specific protein regions, thus providing an adaptive advantage to cancer cells, one can use this feature to identify a driver mutation. This is a crucial need for anyone working in cancer genomics. As you sequence the genome of a cancer cell, you basically find a total mess of mutations, and your job is to distinguish the ones that determine cancer.
One of the current challenges of oncogenomics is to distinguish the genomic alterations that are involved in tumourigenesis (i.e. drivers), from those that give no advantage to cancer cells, but occur stochastically as a by-product of cancer development. (Bioinformatics, 2013)
The lab published a set of tools, actually a real software suite called Oncodrive, to provide a computational method to the identification of cancer mutations. On august the 27th 2014, the group announced the publication of a new member of this suite: OncodriveROLE, and I take this to publish a short resume of the whole suite.
Method to identify cancer drivers from cancer somatic mutations in a cohort of tumors. It computes the bias towards the accumulation of variants with high functional impact (FM bias).
Method to identify genes that accumulate copy number alterations important for tumour development. This is done by computing the functional impact of CNAs by measuring their effect on the expression of the genes affected.
Method to identify genes in which mutations accumulate within specific regions of the protein, which denote events selected by the tumour. It computes a score measuring the mutation clustering of a gene across the protein sequence and compares it with a background model.
Method to classify cancer driver genes into to Activating or Loss of Function roles.
I haven’t tried them since I am working on dystrophy and still have no mutations to detect, but if I got this straight, all the scripts come out as python libraries. Moreover, I really suggest you to visit the lab’s page for tools to find out up to 13 different cancer- dedicated software solutions available for the use.