title: LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer.
abstract: BACKGROUND
  The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots.
  
  
  RESULTS
  We analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family.
  
  
  CONCLUSIONS
  LowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis. LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA.
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. giorgio.melloni@iit.it.
authors: Giorgio E M
authors: GE
authors: Melloni
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. stefano.depretis@iit.it.
authors: Stefano
authors: S
authors: de Pretis
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. laura.riva@iit.it.
authors: Laura
authors: L
authors: Riva
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. mattia.pelizzola@iit.it.
authors: Mattia
authors: M
authors: Pelizzola
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. arnaud.ceol@iit.it.
authors: Arnaud
authors: A
authors: Céol
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. jole.costanza@iit.it.
authors: Jole
authors: J
authors: Costanza
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. heiko.muller@iit.it.
authors: Heiko
authors: H
authors: Müller
authors: Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. luca.zammataro@iit.it.
authors: Luca
authors: L
authors: Zammataro
mesh_terms: D004252:DNA Mutational Analysis / Q000379:methods*; D006801:Humans; D009154:Mutation / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D011506:Proteins / Q000235:genetics / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins