Related Experiment Video
Updated: Jan 14, 2026
08:34
Identification of Virulence Markers of Mycobacterium abscessus for Intracellular Replication in Phagocytes
Published on: September 27, 2018
9.1K
Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a
Ruan Spies1, Derrick W Crook2, Timothy E A Peto3
1Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam; Nuffield Department of Medicine, University of Oxford, Oxford, UK.
The Lancet. Microbe
|October 23, 2025
Summary
Automated whole-genome sequencing (WGS) pipelines for Mycobacterium tuberculosis improve access to diagnostics in low-resource settings. Performance and usability vary, but key features like accessibility and scalability are crucial for implementation in high-burden countries.
Area of Science:
- Genomic epidemiology
- Bioinformatics
- Infectious disease surveillance
Background:
- Complex bioinformatics pipelines hinder whole-genome sequencing (WGS) for Mycobacterium tuberculosis in low-income and middle-income countries (LMICs).
- Automated analysis pipelines offer a solution to improve equitable access to WGS diagnostics and surveillance.
Purpose of the Study:
- To systematically evaluate the performance and usability of publicly available WGS analysis pipelines for M. tuberculosis.
- To assess accuracy, cost, accessibility, and scalability of these pipelines.
Main Methods:
- Searched PubMed and GitHub for automated M. tuberculosis WGS pipelines.
- Assessed accuracy of genotypic drug susceptibility testing (gDST) using phenotypic data.
- Conducted bivariate meta-analysis for pooled sensitivity and specificity.
- Compared lineage classifications and genomic relatedness measures.
Main Results:
- 12 free-to-use pipelines were evaluated (11 Illumina, 4 Nanopore compatible).
- Scalability varied; remote pipelines faced upload limitations, while local pipelines required significant computational resources.
- gDST accuracy was similar across most pipelines; lineage classification was consistent, with minor sublineage differences.
- Genomic relatedness outputs from three pipelines aligned with common thresholds.
Conclusions:
- Numerous automated pipelines can enhance equity in M. tuberculosis WGS.
- Non-functional attributes like availability, accessibility, scalability, and privacy are key differentiators for users in high-burden LMICs.
