ArticleScientific data2023
Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance.Bioinformatics and biology insights · 2026Article
- Lineages of Mycobacterium tuberculosis complex associated with pulmonary drug-resistant tuberculosis in Africa: a systematic review.BMJ public health · 2026Article
- Cross- Wellcome Africa Asian Programmes (AAPs) Acceleration of Genomics for Escalating infectious Diseases, (CAGED) Consortium.Wellcome open research · 2026Article
- The Ethiopian Third National Tuberculosis Drug Resistance Survey Incorporating Whole Genome Sequencing.Open forum infectious diseases · 2025Article
- Whole-genome sequencing analysis to identify antimicrobial resistance regions and virulence factors in Mycobacterium tuberculosis isolates from the Amhara Region, Ethiopia.Scientific reports · 2025Article
- The Case for Genomic Surveillance in Africa.Tropical medicine and infectious disease · 2025Review
- Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa.Scientific data · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycobacterium tuberculosis (MTB) is a pathogenic bacterium accountable for 10.6 million new infections with tuberculosis (TB) in 2021. The fact that the genetic sequences of M. tuberculosis vary widely provides a basis for understanding how this bacterium causes disease, how the immune system responds to it, how it has evolved over time, and how it is distributed geographically. However, despite extensive research efforts, the evolution and transmission of MTB in Africa remain poorly understood. In this study, we used 17,641 strains from 26 countries to create the first curated African Mycobacterium tuberculosis (MTB) classification and resistance dataset, containing 13,753 strains. We identified 157 mutations in 12 genes associated with resistance and additional new mutations potentially associated with resistance. The resistance profile was used to classify strains. We also performed a phylogenetic classification of each isolate and prepared the data in a format that can be used for phylogenetic and comparative analysis of tuberculosis worldwide. These genomic data will extend current information for comparative genomic studies to understand the mechanisms and evolution of MTB drug resistance.
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