Genome-enabled approaches to molecular epidemiology have become essential to public health agencies and the microbial research community. We developed the algorithm STing to provide turn-key solutions for molecular typing and gene detection directly from next generation sequence data of microbial pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the computational overhead associated with the time-consuming steps of quality control, assembly, and alignment, required by more traditional methods. We compared STing to six of the most widely used programs for genome-based molecular typing and demonstrate its ease of use, accuracy, speed and efficiency. STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with larger genome-scale typing schemes, and it enables rapid automated detection of antimicrobial resistance and virulence factor genes. STing determines the sequence type of traditional 7-gene MLST with 100% accuracy in less than 10 seconds per isolate. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

中文翻译：

---

STing：精确且超快速的基因组分析，具有精确的序列匹配。

基因组支持的分子流行病学方法对于公共卫生机构和微生物研究界已经变得至关重要。我们开发了STing算法，可以直接从下一代微生物病原体的序列数据中提供分子识别和基因检测的交钥匙解决方案。我们刺痛的实现采用了创新的ķ-mer搜索策略，消除了较传统方法所需的与质量控制，组装和对齐的耗时步骤相关的计算开销。我们将STing与六种最广泛使用的基于基因组的分子分型程序进行了比较，并证明了STing的易用性，准确性，速度和效率。STing对标准多基因座序列分型方案以及更大的基因组规模分型方案显示出卓越的准确性和性能，并且可以快速自动检测抗菌素耐药性和毒力因子基因。STing可在不到10秒的时间内每个分离株以100％的准确度确定传统7基因MLST的序列类型。我们希望STING的采用将有助于使微生物基因组学民主化，从而最大程度地提高其对公共卫生的利益。