Original Article

Ann Lab Med 2022; 42(2): 213-248

Published online March 1, 2022

Copyright © Korean Society for Laboratory Medicine.

SnackNTM: An Open-Source Software for Sanger Sequencing-based Identification of Nontuberculous Mycobacterial Species

Young-Gon Kim , M.D., Kiwook Jung , M.D., Seunghwan Kim , M.D., Man Jin Kim , M.D., Jee-Soo Lee , M.D., Sung-Sup Park , M.D., Ph.D., and Moon-Woo Seong , M.D., Ph.D.

Department of Laboratory Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea

Correspondence to: Moon-Woo Seong, M.D., Ph.D.
Department of Laboratory Medicine, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: +82-2-2072-4180
Fax: +82-2-747-0359

Received: February 24, 2021; Revised: April 23, 2021; Accepted: September 9, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background: Sequence-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 (
Methods: JAVA 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 rpoB gene region was adjunctively utilized to narrow down the species. The software performance was validated using trace files of 234 clinical cases, comprising 217 consecutive cases and 17 additionally selected cases of unique species.
Results: SnackNTM 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.
Conclusions: SnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.

Keywords: Nontuberculous mycobacteria, NTM identification, SnackNTM, 16S rRNA gene, rpoB