Roger Vargas

The paper predicts antibiotic resistance mutations in Mycobacterium tuberculosis by integrating structural context with mutation data, finding that structural clustering improves prediction accuracy.

The paper identifies SSR INDELs in Mycobacterium tuberculosis that alter antibiotic susceptibility through phylogenomic analysis and experimental validation.

The paper identifies conserved regions in ESX-1 related proteins by mapping SNVs from MTB isolates onto protein structures, highlighting potential therapeutic targets.

The paper develops a gene signature model for TB prognosis and treatment response using network-based meta-analysis and machine learning, validated across 57 studies with high predictive accuracy.

The paper identifies genome-wide mutational dependencies in Mycobacterium tuberculosis evolution, showing how mutations interact to confer antibiotic resistance and detect environmental pressures.

GenTB predicts tuberculosis drug resistance from genomic data using machine learning, achieving higher sensitivity than existing tools with slightly lower specificity.

The paper demonstrates epistatic interactions in Mycobacterium tuberculosis complex that can reverse antibiotic resistance, specifically showing that eis promoter mutations cannot confer resistance if paired with LoF mutations in the eis coding region.

The paper evaluates short-read variant calling accuracy in Mycobacterium tuberculosis using Illumina and PacBio data, finding high precision with optimized mapping thresholds and identifying 68% of excluded repetitive regions as accurately called.

The paper analyzes M. tuberculosis population dynamics during treatment failure using whole-genome sequencing data from clinical isolates to identify resistance mutations and in-host evolutionary patterns.