Respiratory Chain Gene Mutations Linked to Drug-Resistant Tuberculosis

A study analyzing 13,402 Mycobacterium tuberculosis isolates to identify gene mutations associated with multidrug resistance. Whole-genome sequencing was performed, and machine learning models identified specific single nucleotide polymorphisms (SNPs) in respiratory chain genes linked to phylogenetic clustering and MDR development. The dataset was authored by Qiang Ji and last updated in May 2026.

Use Cases

• Identifying genetic markers for drug resistance based on SNP associations mentioned in the description.
• Training predictive models for multidrug-resistant (MDR) tuberculosis based on whole-genome sequencing data.
• Analyzing phylogenetic clustering of resistant strains based on respiratory chain gene mutations.

Strengths

• Includes data from 13,402 Mycobacterium tuberculosis isolates.
• Identifies specific SNPs in genes like atpH, cydA, and qcrB significantly associated with MDR.
• Uses multiple analytical methods (Random Forest, Gradient Boosting, GLMM) as described.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Qiang Ji.

Collection Method

Whole-genome sequencing of Mycobacterium tuberculosis isolates, analyzed with machine learning models.

Time Range

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Freshness

Last updated 2026-05 20 04:38:21; freshness should be verified.

Geography

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