Globally-equitable access to NGS-guided care and prevention of tuberculosis
The TORCH consortium, founded by Prof Van Rie at the University of Antwerp, developed MAGMA (Maximum Accessible Genome for Mtb Analysis), an easy-to-use bioinformatics pipeline specifically created for the analysis of Mtb sequencing data for clinical and public health applications (https://doi.org/10.1371/journal.pcbi.1011648). Even though MAGMA was developed with end-users in mind, the interpretation of resistance profiles and transmission clusters is challenging for doctors and healthcare workers, who typically lack the expertise to interpret these outputs. MAGMA also needs regular updates to remain up-to-date with scientific developments.
To meet these challenges and facilitate the clinical implementation of MAGMA technologies we are working with Sequentia Biotech, a commercial bioinformatics company with experience in clinical bioinformatics pipelines. Following consultations with end-users and stakeholders, Sequentia Biotech integrated MAGMA into their proprietary MICK data infrastructure, to create MICK-MAGMA, a web platform that generates automated, reliable, and actionable outputs. For healthcare workers, we generate comprehensive drug resistance profiles and translate these into the optimal treatment regimens, thus enabling the use of precision medicine for drug resistant TB (DR-TB). For regional and national DR-TB reference laboratories, we are working together with EPCON to create a dashboard that monitors resistance to ‘old’ TB drugs (such as rifampicin, isoniazid, pyrazinamide and fluoroquinolones) and emergence of resistance to new TB drugs (including bedaquiline, linezolid, delanamid and pretonamid). For contact tracing units, MICK-MAGMA will translate the phylogenetic data into a dynamic visualisation of transmission events and will identify superspreading events to enable targeted public health interventions, thus enabling a precision public health approach to RR-TB control.
actionable Whole genome sequencing data
MICK-MAGMA generates actionable drug resistance interpretations for all drugs for TB care. Variants without a statistical association with resistance are assigned a probability of resistance
adaptable to different ngs data inputs
MICK-MAGMA has integrated workflows for other NGS diagnostic assays, including Deeplex, allowing it to be adaptable to TB diagnostic cascades in a wide range of health care contexts.
