Mycobacterium Tuberculosis Respiratory Chain Mutations Linked to Drug Resistance

A study analyzing 13,402 isolates of Mycobacterium tuberculosis to identify genetic mutations associated with drug resistance. The dataset, authored by Qiang Ji and last updated in May 2026, uses whole-genome sequencing and machine learning models to link specific single nucleotide polymorphisms in respiratory chain genes to multidrug-resistant and single-drug-resistant isolates.

Use Cases

• Identifying genetic markers for drug resistance based on whole-genome sequencing data.
• Training predictive models for multidrug-resistant tuberculosis classification using identified SNPs.
• Analyzing phylogenetic clustering patterns of resistant isolates based on respiratory chain gene mutations.

Strengths

• Includes 13,402 Mycobacterium tuberculosis isolates, providing a substantial sample size.
• Identifies specific SNPs (e.g., atpH A428G, cydA C942A) significantly associated with drug resistance and phylogenetic clustering.
• Uses multiple analytical methods (Random Forest, Gradient Boosting, GLMM) for robust mutation identification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (43.2 KB), suggesting it may contain summary or processed results rather than raw sequence data.

Provenance

Source

figshare

Collection Method

Whole-genome sequencing performed on analyzed strains of M. tuberculosis.

Time Range

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Freshness

Last updated 2026-05-20 04:38:20; freshness should be verified.

Geography

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