SSTAR, a Stand-Alone Easy-To-Use Antimicrobial Resistance Gene Predictor

Abstract

We present the easy-to-use Sequence Search Tool for Antimicrobial Resistance, SSTAR. It combines a locally executed BLASTN search against a customizable database with an intuitive graphical user interface for identifying antimicrobial resistance (AR) genes from genomic data. Although the database is initially populated from a public repository of acquired resistance determinants (i.e., ARG-ANNOT), it can be customized for particular pathogen groups and resistance mechanisms. For instance, outer membrane porin sequences associated with carbapenem resistance phenotypes can be added, and known intrinsic mechanisms can be included. Unique about this tool is the ability to easily detect putative new alleles and truncated versions of existing AR genes. Variants and potential new alleles are brought to the attention of the user for further investigation. For instance, SSTAR is able to identify modified or truncated versions of porins, which may be of great importance in carbapenemase-negative carbapenem-resistant Enterobacteriaceae. SSTAR is written in Java and is therefore platform independent and compatible with both Windows and Unix operating systems. SSTAR and its manual, which includes a simple installation guide, are freely available from https://github.com/tomdeman-bio/Sequence-Search-Tool-for-Antimicrobial-Resistance-SSTAR-. IMPORTANCE Whole-genome sequencing (WGS) is quickly becoming a routine method for identifying genes associated with antimicrobial resistance (AR). However, for many microbiologists, the use and analysis of WGS data present a substantial challenge. We developed SSTAR, software with a graphical user interface that enables the identification of known AR genes from WGS and has the unique capacity to easily detect new variants of known AR genes, including truncated protein variants. Current software solutions do not notify the user when genes are truncated and, therefore, likely nonfunctional, which makes phenotype predictions less accurate. SSTAR users can apply any AR database of interest as a reference comparator and can manually add genes that impact resistance, even if such genes are not resistance determinants per se (e.g., porins and efflux pumps).

Keywords: BLAST; SSTAR; antimicrobial resistance genes; porins.

FIG 1

Graphical user interface of SSTAR. The top two buttons are used for uploading the genome assembly and AR gene collection. The user also fills out a sequence similarity cutoff value for detecting potential new variants.

FIG 2

Composition of the modified SSTAR SRST2 ARG-ANNOT antimicrobial resistance database. We manually included wild-type outer membrane porin sequences for Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, and Enterobacter aerogenes and excluded tetR and bla_OXA-30.

FIG 3

Several gene groups and associated alleles designated by SRST2 ARG-ANNOT. Each group is assigned a unique number at the beginning of the sequence header; e.g., bla_OXA-48 alleles start with 275, and bla_KPC alleles start with 184, etc.