{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Espitia-Navarro HF"],"funding":["IHRC-Georgia Tech Applied Bioinformatics Laboratory"],"pagination":["7681-7689"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7430640"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["48(14)"],"pubmed_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."],"journal":["Nucleic acids research"],"pubmed_title":["STing: accurate and ultrafast genomic profiling with exact sequence matches."],"pmcid":["PMC7430640"],"funding_grant_id":["RF383"],"pubmed_authors":["Jordan IK","Espitia-Navarro HF","Chande AT","Smith H","Nagar SD","Rishishwar L"],"additional_accession":[]},"is_claimable":false,"name":"STing: accurate and ultrafast genomic profiling with exact sequence matches.","description":"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.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Aug","modification":"2025-04-25T19:41:36.639Z","creation":"2025-04-06T08:02:48.445Z"},"accession":"S-EPMC7430640","cross_references":{"pubmed":["32619234"],"doi":["10.1093/nar/gkaa566"]}}