Simulated M. tuberculosis genomic data for variant identification performance testing
Abstract/Contents
- Abstract
- Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet there is no current consensus on how to measure genomic variation. We simulated M. tuberculosis genomic sequence data in order to measure performance of variant identification tools in (a) recovering true genome-wide variants and (b) recovering true pairwise differences between closely related outbreak genomes. Here, we include the simulated genomic data and truth VCFs for further benchmarking studies.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | January 2020 |
Creators/Contributors
| Author | Walter, Katharine S. |
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Subjects
| Subject | Mycobacterium tuberculosis |
|---|---|
| Subject | variant calling |
| Subject | genomics |
| Subject | molecular epidemiology |
| Genre | Dataset |
Bibliographic information
| Related item |
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| Location | https://purl.stanford.edu/mr554nj9219 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Walter KS, Colijn C, Cohen T, Mathema B, Liu Q, Bowers J, et al. Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference. bioRxiv. 2019;733642.
Collection
Stanford Research Data
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- Contact
- kwalter@stanford.edu
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