henhaobio
Sign in

bioRxivMicrobiologyPreprint

Improved subspecies identification in clinical Mycobacterium abscessus complex isolates using whole genome sequence

D. Lin, R. Sun, Y. Tan, J. Wang, X. Chen

PDFDOI

Abstract

Mycobacterium abscessus complex, which is frequently reported causing a variety of skin and soft tissues diseases in humans, is composed of three subspecies, namely M. abscessus subsp. abscessus, M. abscessus subsp. massiliense and M. abscessus subsp. bolletii. Currently, the differentiation of these three subspecies in clinical isolates still largely depend on single gene identification methods like the genes namely hsp65, 16s with a limited accuracy. This study confirmed the limitations of the single gene based method in the subspecies identification. We performed a comprehensive analysis of MABC genomes in the NCBI database and tried to build an accurate and user-friendly identify method. Here, we describe an improved assay for Mycobacterium abscessus complex fast identification using WGS data, based on the identities of rpoB, erm(41) and rpls. Comprehensive analysis has been performed to compare our software results with the traditional method. The result showed that the method built-in this study could 100% identification the subspecies for the Mycobacterium abscessus complex in the public genome database (893 genomes from NCBI database and 6 clinical isolates from this study). Because this software can be easily integrated into a routine workflow to quickly and precisely provide subspecies-level identification and discrimination MABC different subspecies in clinical isolates by WGS. This assay will facilitate accurate molecular identification of species from the MABC complex in a variety of clinical specimens and diagnostic contexts.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISWang '22Rodriguez-T '22Yoshida '20Robinne '21Lipworth '18Bongulto '19
81% semantic matchMicrobiology
H.-Y. Wang, C.-H. Kuo, C.-R. Chung+4 more·bioRxiv·Sep 6, 2022