An approach to identifying drug resistance associated mutations in bacterial strains

Drug resistance in bacterial pathogens is an increasing problem that stimulates
research activity. However, still our understanding of drug resistance 
mechanisms remains incomplete. One promising approach to deepen understanding 
drug resistance mechanisms is to use whole-genome sequences to identify genetic 
mutations associated with drug resistance phenotypes for bacterial strains. 
	
In this work, we present a new computational method to identyfying drug
resistance associated mutations in bacterial strains. In this approach, 
the genotype data consist of gene gain/loss profiles, derived from gene 
families, and point mutation profiles which are determined from multiple 
alignments of the considered gene families. The method employs a score, 
which we call weighted support, and an assignment of statistical significance to it. 

We tested our method on collected genotype and phenotype data (from over 50 
publications) of 100 fully sequenced S. aureus strains and 10 commonly 
used drugs. Our computational experiments show that by employing our approach 
we were able to successfully re-identify most of the known drug resistance 
determinants. We also argue that the concept of weighted support, by utilizing 
a phylogenetic information, yields results which fit better the goal of detecting 
drug resistant associated mutaions than some other scores like the ODDS ratio.  
As a result of applying our methodology we identified some putative 
novel associations.