Richard Liwski

TL;DR: This study provides a standardised and comprehensive approach for the interpretation of mutations as predictors of M. tuberculosis drug- resistant phenotypes and has implications for the clinical interpretation of molecular diagnostics and next-generation sequencing as well as efficient individualised therapy for patients with drug-resistant tuberculosis.

TL;DR: A global partnership of academic institutions, public health agencies, and nongovernmental organizations has been established to develop a tuberculosis relational sequencing data platform (ReSeqTB) that seeks to increase understanding of the genetic basis of resistance by correlating molecular data with results from drug susceptibility testing and, optimally, associated patient outcomes.

TL;DR: A unified analysis variant pipeline (UVP) was developed to identify variants and assign lineage from MTBC sequence data and has an above 94% accuracy of predicting drug based on the accompanying DST results in the ReSeqTB platform.

TL;DR: The RDCA-DAP curates, standardizes, and tags data across rare disease datasets to make it findable within the database, and contains a built-in analytics platform to help visualize, interpret, and use it to support drug development.