A Comparative Evaluation of Four Bioinformatic Tools for Identifying HIV-1 pol Drug Resistance Mutations Using Illumina MiSeq Data

The transition from Sanger to next-generation sequencing (NGS) for HIV-1 drug resistance testing offers enhanced sensitivity but also introduces bioinformatic variability. This study evaluated four strategies: the commercial Exatype platform, the academic Stanford HIVdb-NGS, the open-source Quasitools (HyDRA) suite, and a custom de novo assembly workflow, iLunaR. Using 85 clinical HIV-1 <i>pol</i> MiSeq sequencing datasets, concordance was assessed at a 2% mutation detection threshold. A majority consensus standard defined true presence if a mutation was detected by at least three pipelines and supported by Sanger sequencing. While the datasets were successfully processed by all pipelines, discordances emerged in detecting low-abundance mutations and a specific case of structural mutation. iLunaR achieved perfect agreement (Cohen's kappa = 1.000; 95% CI: 1.000-1.000). Quasitools demonstrated the lowest agreement (Cohen's kappa = 0.901; 95% CI: 0.807-0.995) due to consistent reporting of mutations at lower abundance levels and aligner-induced reference bias misclassifying a deletion as a point mutation. Exatype (Cohen's kappa = 0.951; 95% CI: 0.884-1.000) and Stanford (Cohen's kappa = 0.926; 95% CI: 0.846-1.000) exhibited specific failures, including an omitted integrase mutation and codon translation errors, respectively. These findings confirm that bioinformatic algorithm choice remains a critical clinical variable despite NGS advancements in HIV-1 drug resistance testing.