1. STing: accurate and ultrafast genomic profiling with exact sequence matches
- Author
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Hector F. Espitia-Navarro, Aroon T. Chande, I. King Jordan, Shashwat Deepali Nagar, Lavanya Rishishwar, and Heather C. Smith
- Subjects
Genomic profiling ,Virulence Factors ,AcademicSubjects/SCI00010 ,Computational biology ,Biology ,Genome ,03 medical and health sciences ,Molecular typing ,Genetics ,Typing ,030304 developmental biology ,0303 health sciences ,030306 microbiology ,High-Throughput Nucleotide Sequencing ,Computational Biology ,Drug Resistance, Microbial ,Genomics ,eye diseases ,Sting ,Microbial genomics ,Multilocus sequence typing ,Genes, Microbial ,Algorithms ,Software ,Multilocus Sequence Typing ,Next generation sequence - Abstract
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.
- Published
- 2020
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