Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis.
Amidohydrolases
/ genetics
Antitubercular Agents
/ pharmacology
Computational Biology
/ instrumentation
DNA, Bacterial
/ genetics
Datasets as Topic
Drug Resistance, Bacterial
/ genetics
Genome, Bacterial
/ genetics
Humans
Internet
Microbial Sensitivity Tests
/ methods
Mutation
Mycobacterium tuberculosis
/ genetics
Pyrazinamide
/ pharmacology
Software
Tuberculosis
/ drug therapy
Whole Genome Sequencing
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
19
10
2018
accepted:
09
02
2019
entrez:
1
3
2019
pubmed:
1
3
2019
medline:
18
12
2019
Statut:
epublish
Résumé
Automated online software tools that analyse whole genome sequencing (WGS) data without the need for bioinformatics expertise can motivate the implementation of WGS-based molecular drug susceptibility testing (DST) in routine diagnostic settings for tuberculosis (TB). Pyrazinamide (PZA) is a key drug for current and future TB treatment regimens; however, it was reported that predictive power for PZA resistance by the available tools is low. Therefore, this low predictive power may make users hesitant to use the tools. This study aimed to elucidate why and to uncover the real performance of the tools when taking into account their variation calling lists (manual inspection), not just their automated reporting system (default setting) that was evaluated by previous studies. WGS data from 191 datasets comprising 108 PZA-resistant and 83 susceptible strains were used to evaluate the potential performance of the available online tools (TB Profiler, TGS-TB, PhyResSE, and CASTB) for predicting phenotypic PZA resistance. When taking into consideration the variation calling lists, 73 variants in total (47 non-synonymous mutations and 26 indels) in pncA were detected by TGS-TB and PhyResSE, covering all mutations for the 108 PZA-resistant strains. The 73 variants were confirmed by Sanger sequencing. TB Profiler also detected all but three complete loss, two large deletion at the 3'-end, and one relatively large insertion of pncA. On the other hand, many of the 73 variants were lacking in the automated reporting systems except by TGS-TB; of these variants, CASTB detected only 20. By applying the 'non-wild type sequence' approach for predicting PZA resistance, accuracy of the results significantly improved compared with that of the automated results obtained by each tool. Users can obtain more accurate predictions for PZA resistance than previously reported by manually checking the results and applying the 'non-wild type sequence' approach.
Identifiants
pubmed: 30817803
doi: 10.1371/journal.pone.0212798
pii: PONE-D-18-30360
pmc: PMC6394917
doi:
Substances chimiques
Antitubercular Agents
0
DNA, Bacterial
0
Pyrazinamide
2KNI5N06TI
Amidohydrolases
EC 3.5.-
PncA protein, Mycobacterium tuberculosis
EC 3.5.-
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0212798Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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