LowMACA

LowMACA (Low frequency Mutations Analysis via Consensus Alignment) is a software tool to uncover potential driver genes in cancer by analyzing low-frequency mutations within the exact functional domains across various proteins. By aggregating mutations from proteins sharing functional domains, LowMACA identifies conserved residues with clustered mutations, helping to visualize and statistically assess mutational hotspots that may act as driver mutations. The tool has been effectively used to analyze the Ras superfamily, discovering new putative driver mutations and genes in less studied subfamilies like Rho, Rab, and Rheb. Additionally, LowMACA's approach has revealed that genes considered to be of low confidence exhibit mutation patterns similar to those of high-confidence genes within the same protein family.

Topic

Sequence analysis;Proteins

Detail

  • Operation: Multiple sequence alignment;Protein sequence analysis;Sequence profile generation

  • Software interface: Command-line user interface,Library

  • Language: R

  • License: The GNU General Public License v3.0

  • Cost: Free

  • Version name: 1.31.0

  • Credit: Fondazione Cariplo

  • Input: Pfam accession number [Textual format], Gene ID [Textual format]

  • Output: Plot [png] [Textual format] [Image format], Sequence alignment (protein) [png] [Textual format] [Image format], Sequence composition plot [png] [Textual format] [Image format], Sequence alignment report [png] [Textual format] [Image format]

  • Contact: Giorgio Melloni melloni.giorgio@gmail.com,StefanodePretis

  • Collection: -

  • Maturity: Stable

Publications

  • LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer.
  • Melloni GE, et al. LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer. LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer. 2016; 17:80. doi: 10.1186/s12859-016-0935-7
  • https://doi.org/10.1186/s12859-016-0935-7
  • PMID: 26860319
  • PMC: PMC4748640

Download and documentation

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