ArticleAnnals of laboratory medicine2022
SnackNTM: An Open-Source Software for Sanger Sequencing-based Identification of Nontuberculous Mycobacterial Species.
Article in Annals of laboratory medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Retrospective analysis of non-tuberculous mycobacterial skin infections: diagnostic accuracy, treatment efficacy, and PCR-based species detection in a Chinese primary care hospital.Journal of clinical microbiology · 2025Article
- Comparative evaluation of MALDI-ToF mass spectrometry and Sanger sequencing of theFrontiers in cellular and infection microbiology · 2025Article
- Review
- Evaluation of mycobacteria infection prevalence and optimization of the identification process in North Sardinia, Italy.Microbiology spectrum · 2024Article
- MALDI-TOF Mass Spectrometry as a Rapid Screening Alternative for Non-tuberculous Mycobacterial Species Identification in the Veterinary Laboratory.Frontiers in veterinary science · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSequence-based identification is one of the most effective methods for species-level identification of nontuberculous mycobacteria (NTM). However, it is time-consuming because of the bioinformatics processes involved, including sequence trimming, consensus sequence generation, and public database searches. We developed a simple and fully automated software that enabled species-level identification of NTM from trace files, SnackNTM (https://github.com/Young-gonKim/SnackNTM).
methodsJAVA programing language was used for software development. The SnackNTM diagnostic algorithm utilized 16S rRNA gene sequences, according to the Clinical & Laboratory Standards Institute guidelines, and an
resultsSnackNTM could analyze multiple cases at once, and all the bioinformatics processes required for sequence-based NTM identification were automatically performed with a single mouse click. SnackNTM successfully identified 95.9% (208/217) of consecutive clinical cases, and the results showed 99.0% (206/208) agreement with manual classification results. SnackNTM successfully identified all 17 cases of unique species. In a processing time comparison test, the analysis and reporting of 30 cases, which took 150 minutes manually, took only 40 minutes with SnackNTM.
conclusionsSnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.
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