A genus-wide whole genome multi-locus sequence typing (wgMLST) schema for typing Brucella spp. in BioNumerics is now available! When used in combination with our cloud-based Calculation Engine, typing Brucella spp. isolates up to strain level using whole genome sequencing data is now easily accessible to everyone.
What is the schema exactly?
Based on the known diversity within the 11 species of the genus Brucella spp., all genus-wide loci are grouped together in a wgMLST subset, further called “trunk”. Starting from this trunk, species-specific loci were added to create a pan-genomic schema for each Brucella species. While the trunk already allows for interspecies discrimination, the addition of the accessory loci increases the intraspecies discriminatory power. At the same time, the extended schemas also allow for the detection of subtype- or outbreak-specific markers, thus enabling more powerful classification and outbreak definition tools. Check out our tips on how to make the most of the Brucella spp. wgMLST schema.
Which loci are present?
|Number of trunk loci||3246|
|Number of accessory loci||2058|
|Number of MLST loci||21|
|Total number of loci||5325|
How will it help you?
By using BioNumerics and the integrated powerful calculation infrastructure, analyzing whole genome sequencing data for Brucella spp. has become a lot more straightforward. Our cloud-based Calculation Engine offers a high-throughput environment for all your sample processing needs. Its quality-controlled de novo assembly possibilities allow you to easily assemble whole genome sequencing data without the need of local computing power. The two allele detection procedures (assembly-based and assembly-free) allow you to perform fast and reliable allele calling for e.g. cluster detection which can be combined with whole genome SNP analysis to obtain the utmost resolution within your sample comparisons.
The BioNumerics wgMLST schema for Brucella spp. has been tested, validated and approved by our microbiologists. Great care has been taken to create an analysis procedure that minimizes sample artifacts, while maintaining an enormous discriminatory power. With turnaround times of less than 30 minutes per sample and the ability to process many samples simultaneously, the power of high-performance computing will be brought to your desktop with a few clicks.
Try it on your own data now!
To start using this wgMLST approach for typing of Brucella spp., simply request a Calculation Engine project. For an easy introduction, we have a Brucella spp. wgMLST tutorial available online. We look forward to your discoveries!
1: Whatmore, A. M., Perrett, L. L., & MacMillan, A. P. (2007). Characterisation of the genetic diversity of Brucella by multilocus sequencing. BMC microbiology, 7(1), 34.
2: Whatmore, A. M., Koylass, M. S., Muchowski, J., Edwards-Smallbone, J., Gopaul, K. K., & Perrett, L. L. (2016). Extended Multilocus Sequence Analysis to Describe the Global Population Structure of the Genus Brucella: Phylogeography and Relationship to Biovars. Frontiers in Microbiology, 7.