Evidence map›Paper›PMID 38643090›Full record

ArticleBMC genomics2024

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes.

Doyoung Kim, Jeong-Ih Shin, In Young Yoo, Sungjin Jo, Jiyon Chu, Woo Young Cho, Seung-Hun Shin, Yeun-Jun Chung, Yeon-Joon Park, Seung-Hyun Jung

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Sentinel-Site-Based Surveillance ofAntibiotics (Basel, Switzerland) · 2025
    Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Doyoung Kim *Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Jeong-Ih Shin *Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea.
In Young YooDepartment of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Sungjin JoDepartment of Laboratory Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Jiyon ChuDepartment of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Woo Young ChoConnectaGen, Hanam, Korea.
Seung-Hun ShinConnectaGen, Hanam, Korea.
Yeun-Jun ChungDepartment of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Yeon-Joon ParkDepartment of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Seung-Hyun JungDepartment of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea. hyun@catholic.ac.kr.
Catholic University of Korea · KRSeoul St. Mary's Hospital · KR

Funding

Korea Health Technology R&D Project grant through the Korea Health Industry Development Institute HI22C0226
6 · The paper itself

Abstract

backgroundDrug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options.

resultsWe developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8-96.7%) to 79.6% for pyrazinamide (95% CI 76.9-82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1-100.0%) to 74.9% for capreomycin (95% CI 69.3-80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8-99.3%) to 79.5% for ethionamide (95% CI 76.4-82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC.

conclusionsGenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org .

Indexed as

Mycobacterium tuberculosisTuberculosis, Multidrug-ResistantAntitubercular AgentsDrug ResistanceHumansInternetMicrobial Sensitivity TestsNontuberculous MycobacteriaAntitubercular AgentsDrug susceptibility testingMycobacteria species identificationMycobacterium tuberculosis complexNon-tuberculosis mycobacteriaWhole genome sequencing

Identifiers

PMID38643090
PMCPMC11031912
OpenAlexW4394976078

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.