On December 19th we have a guest speaker: Donate Weghorn from the Centre of Genomic Regulation, Barcelona will talk about Probabilistic approaches to inference of mutation rate and selection in cancer.
Date: Thursday, December 19th
Time: 4 p.m.
Venue: IRI Life Sciences, Humboldt-Universität zu Berlin,
Philippstr. 13, Michaelis Building (No 18),
Maud Menten Hall (3rd Floor)
https://goo.gl/maps/9LUWXKXj6pv
Probabilistic approaches to inference of mutation rate and selection in cancer
Cancer is a highly complex system that evolves asexually under high mutation rates and strong selective pressures. Cancer genomics efforts have identified genes and regulatory elements driving cancer development and neoplastic progression. The detection of both significantly mutated (positive selection) and undermutated (negative selection) genes is completely confounded by the genomic heterogeneity of the cancer mutation rate. Here, I present an approach to address mutation rate heterogeneity in order to increase the power and accuracy of selection inference. Using a hierarchical model, we infer the distribution of mutation rates across genes that underlies the observed distribution of the synonymous mutation count within a given cancer type. This enables the inference of the probability of nonsynonymous mutations without additional parameters, however explicitly taking into account cancer-type-specific mutational signatures, which are known to be highly distinct. We then augmented our test through integrating information at the single-nucleotide level. Based on a model that accounts for the extended sequence context (> 5-mers) around mutated sites, this second component of the test identifies genes with an excess of mutations in specific nucleotide contexts, which deviate from the characteristic context around neutrally evolving passenger mutations. Using the combined test, we discovered a catalogue of well-known cancer driver genes as well as a long tail of novel candidate cancer genes with mutation frequencies as low as 1% and functional supporting evidence.
