Dataset Information

GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification.

ABSTRACT: Whole genome sequencing (WGS) of clinical bacterial isolates has the potential to transform the fields of diagnostics and public health. To realize this potential, bioinformatic software that reports identification results needs to be developed that meets the quality standards of a diagnostic test. We developed GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking) using k-mer based strategies for identification of bacteria based on WGS reads. GAMBIT incorporates this algorithm with a highly curated searchable database of 48,224 genomes. Herein, we describe validation of the scoring methodology, parameter robustness, establishment of confidence thresholds and the curation of the reference database. We assessed GAMBIT by way of validation studies when it was deployed as a laboratory-developed test in two public health laboratories. This method greatly reduces or eliminates false identifications which are often detrimental in a clinical setting.

SUBMITTER: Lumpe J

PROVIDER: S-EPMC9934365 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification.

Lumpe Jared J Gumbleton Lynette L Gorzalski Andrew A Libuit Kevin K Varghese Vici V Lloyd Tyler T Tadros Farid F Arsimendi Tyler T Wagner Eileen E Stephens Craig C Sevinsky Joel J Hess David D Pandori Mark M

PloS one 20230216 2

Whole genome sequencing (WGS) of clinical bacterial isolates has the potential to transform the fields of diagnostics and public health. To realize this potential, bioinformatic software that reports identification results needs to be developed that meets the quality standards of a diagnostic test. We developed GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking) using k-mer based strategies for identification of bacteria based on WGS reads. GAMBIT incorporates this al ...[more]

PMID: 36795668

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Project description:Many ecological experiments are based on the extraction and downstream analyses of microorganisms from different environmental samples. Due to its high throughput, cost-effectiveness and rapid performance, Matrix Assisted Laser Desorption/Ionization Mass Spectrometry with Time-of-Flight detector (MALDI-TOF MS), which has been proposed as a promising tool for bacterial identification and classification, could be advantageously used for dereplication of recurrent bacterial isolates. In this study, we compared whole-cell MALDI-TOF MS-based analyses of 49 bacterial cultures to two well-established bacterial identification and classification methods based on nearly complete 16S rRNA gene sequence analyses: a phylotype-based approach, using a closest type strain assignment, and a sequence similarity-based approach involving a 98.65% sequence similarity threshold, which has been found to best delineate bacterial species. Culture classification using reference-based MALDI-TOF MS was comparable to that yielded by phylotype assignment up to the genus level. At the species level, agreement between 16S rRNA gene analysis and MALDI-TOF MS was found to be limited, potentially indicating that spectral reference databases need to be improved. We also evaluated the mass spectral similarity technique for species-level delineation which can be used independently of reference databases. We established optimal mass spectral similarity thresholds which group MALDI-TOF mass spectra of common environmental isolates analogically to phylotype- and sequence similarity-based approaches. When using a mass spectrum similarity approach, we recommend a mass range of 4-10 kDa for analysis, which is populated with stable mass signals and contains the majority of phylotype-determining peaks. We show that a cosine similarity (CS) threshold of 0.79 differentiate mass spectra analogously to 98.65% species-level delineation sequence similarity threshold, with corresponding precision and recall values of 0.70 and 0.73, respectively. When matched to species-level phylotype assignment, an optimal CS threshold of 0.92 was calculated, with associated precision and recall values of 0.83 and 0.64, respectively. Overall, our research indicates that a similarity-based MALDI-TOF MS approach can be routinely used for efficient dereplication of isolates for downstream analyses, with minimal loss of unique organisms. In addition, MALDI-TOF MS analysis has further improvement potential unlike 16S rRNA gene analysis, whose methodological limits have reached a plateau.

Project description:BACKGROUND:Bacterial meningitis is a life-threatening infection that remains a public health concern. Bacterial meningitis is commonly caused by the following species: Neisseria meningitidis, Streptococcus pneumoniae, Listeria monocytogenes, Haemophilus influenzae and Escherichia coli. Here, we describe BMScan (Bacterial Meningitis Scan), a whole-genome analysis tool for the species identification of bacterial meningitis-causing and closely-related pathogens, an essential step for case management and disease surveillance. BMScan relies on a reference collection that contains genomes for 17 focal species to scan against to identify a given species. We established this reference collection by supplementing publically available genomes from RefSeq with genomes from the isolate collections of the Centers for Disease Control Bacterial Meningitis Laboratory and the Minnesota Department of Health Public Health Laboratory, and then filtered them down to a representative set of genomes which capture the diversity for each species. Using this reference collection, we evaluated two genomic comparison algorithms, Mash and Average Nucleotide Identity, for their ability to accurately and rapidly identify our focal species. RESULTS:We found that the results of Mash were strongly correlated with the results of ANI for species identification, while providing a significant reduction in run-time. This drastic difference in run-time enabled the rapid scanning of large reference genome collections, which, when combined with species-specific threshold values, facilitated the development of BMScan. Using a validation set of 15,503 genomes of our species of interest, BMScan accurately identified 99.97% of the species within 16 min 47 s. CONCLUSIONS:Identification of the bacterial meningitis pathogenic species is a critical step for case confirmation and further strain characterization. BMScan employs species-specific thresholds for previously-validated, genome-wide similarity statistics compiled from a curated reference genome collection to rapidly and accurately identify the species of uncharacterized bacterial meningitis pathogens and closely related pathogens. BMScan will facilitate the transition in public health laboratories from traditional phenotypic detection methods to whole genome sequencing based methods for species identification.

Dataset Information

GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification.

Publications

GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification.

Similar Datasets

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