Alex Rosenthal

Complete genomes from 151 clinical M. tuberculosis isolates show gene conversion between paralogs creating diversity hotspots in PE/PPE/ESX genes and T cell antigens, including PPE18.

The paper evaluates pan-genome analysis methods using M. tuberculosis isolates, finding that annotation discrepancies and assembly quality affect results, and introduces panqc to address these issues.

This study shows that culturing Mycobacterium tuberculosis from sputum samples accurately mirrors the genetic diversity found in the original clinical samples, with high variant agreement and correlation.

The paper compares machine learning algorithms for predicting antimicrobial peptides (AMPs) specific to microbial strains, finding Random Forest and AdaBoost perform best with improved accuracy using genome characteristics.

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.