Performances of bioinformatics tools for the analysis of sequencing data of Mycobacterium tuberculosis complex strains

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Abstract Whole genome sequencing of Mycobacterium tuberculosis complex (MTBC) strains is a new and rapidly growing tool to obtain results regarding resistance, virulence factors and phylogeny of the strains. Bioinformatics tools presented as user-friendly and easy to use are available online. The objective of this work was to evaluate the performances of two bioinformatics tools, easily accessible on the internet, for the analysis of sequencing data of MTBC strains. Two hundred and twenty-seven MTBC strains isolated at the laboratory of the Avicenne Hospital between 2015 and 2021 were sequenced using Illumina®(USA) MiSeq technology. An analysis of the sequencing data was performed using the two tools Mykrobe and PhyResSE. Sequencing quality, resistance or susceptibility status and phylogeny were investigated for each strain. Genotypic resistance results were compared to the results obtained by phenotypic drug susceptibility testing performed in the hospital’s routine laboratory. Using the PhyResSE tool we found an average coverage of 98% against the reference strain H37Rv and an average depth of 119X. No information on sequencing quality was obtained with the Mykrobe tool. The concordance of each tool with the phenotypic method for determining susceptibility to first-line anti-tuberculosis drugs was 95%. Mykrobe and PhyResSE tools identified resistance to second-line anti-tuberculosis drugs in 5.3% and 5.7% of cases respectively. The sensitivity and specificity of each tool compared to the phenotypic method was respectively 70% and 98% for Mykrobe and 76% and 97% for PhyResSE. Finally, the two tools showed 99.5% agreement in lineage determination. The Mykrobe and PhyResSE bioinformatics tools were easy to use, fast and efficient. The Mykrobe tool had the advantage of being offline and its interface was more user-friendly. The use of these platforms depends on their accessibility and updating. However, their use is accessible to people not trained in bioinformatics and would allow a complementary approach to phenotypic methods for the study of MTBC strains.

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