Welcome to LowMACA (Low frequency Mutation Analysis via Consensus Alignment)
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. By aligning the protein sequences of selected genes targeted by mutations and piling up these mutations according to the relative position derived from the alignment, LowMACA can dissect the whole protein family and assess the statistical significance of specific clusters of mutations.
LowMACA comes as an R package that implements a suite of method, plots and statistics to study the mutations affecting gene families or pfam domains across the cancer genomes. The package is also wrapped into a shiny application including more interactive plots and new functionalities.
LowMACA Bioconductor Package
LowMACA is a Bioconductor R package available HERE
LowMACA Bioconductor Package Vignette
How to install and use all the functionalities of LowMACA, HERE
LowMACA Shiny App
Stable version of LowMACA is available as a Shiny based GUI HERE
Development version can be found on gmelloni GitHub
LowMACA Shiny App documentation
Instruction to install the app plus infographic and video tutorial to use it HERE
