title: GWAMAR: genome-wide assessment of mutations associated with drug resistance in bacteria.
abstract: BACKGROUND
  Development of drug resistance in bacteria causes antibiotic therapies to be less effective and more costly. Moreover, our understanding of the process remains incomplete. One promising approach to improve our understanding of how resistance is being acquired is to use whole-genome comparative approaches for detection of drug resistance-associated mutations.
  
  
  RESULTS
  We present GWAMAR, a tool we have developed for detecting of drug resistance-associated mutations in bacteria through comparative analysis of whole-genome sequences. The pipeline of GWAMAR comprises several steps. First, for a set of closely related bacterial genomes, it employs eCAMBer to identify homologous gene families. Second, based on multiple alignments of the gene families, it identifies mutations among the strains of interest. Third, it calculates several statistics to identify which mutations are the most associated with drug resistance.
  
  
  CONCLUSIONS
  Based on our analysis of two large datasets retrieved from publicly available data for M. tuberculosis, we identified a set of novel putative drug resistance-associated mutations. As a part of this work, we present also an application of our tool to detect putative compensatory mutations.
authors.forename: Michal
authors.initials: M
authors.lastname: Wozniak
authors.forename: Jerzy
authors.initials: J
authors.lastname: Tiuryn
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D004252:DNA Mutational Analysis / Q000379:methods*; D030541:Databases, Genetic; D024881:Drug Resistance, Bacterial; D016680:Genome, Bacterial; D055106:Genome-Wide Association Study; D015233:Models, Statistical; D005810:Multigene Family; D009169:Mycobacterium tuberculosis / Q000235:genetics*; D010802:Phylogeny; D017354:Point Mutation; D012984:Software