LowMACA

The remarkable efforts recently carried out to fully understand the mutational landscape of various kinds of cancer have revealed that the mutational processes can be extremely variable depending on the tumor type and the clinical features of patients. Thanks to these efforts, we have reached a clear picture of the most commonly mutated genes in various types of cancer. However, we are still far from a complete picture of rarely mutated genes, which can be an important target for personalized medicine.

To overcome this difficulty, we have implemented LowMACA (Low frequency Mutation Analysis via Consensus Alignment), a new method able to assess specific characteristics of rarely mutated genes that show patterns of positive selection. LowMACA aggregates and analyzes the mutational patterns of several genes whose encoded proteins have a high level of sequence similarity or share specific protein domains.