Detection and analysis of drug resistance mutations: Bioinformatics challenges and solutions.

Poster number: 13

Mahanandeeshwar Gattu, Junzhou Li, William Burtle, Bill Marshall, Roslyn Nilson, Craig Meyer, Andrew Theaker, Margaret Tisdale, Karen Biron, Lynn Condreay, Lisa Ross, Janna Scott and Clinical Virology Database Team

  1. Research & Development, GlaxoSmithkline, USA & UK.

Anti-viral drugs exert a selective pharmacological pressure on the viral genome which can result in complex patterns of drug specific and position specific resistance mutations. This is especially the case for HIV and hepatitis viruses that exist within their host as a heterogeneous mix of viral variants known as quasispecies. The efficacy of anti-viral drugs can depend on the presence or absence of certain mutations in the viral genome. Therefore, understanding the relationship between mutations, drug resistance and clinical response are critical requirements in designing more effective therapeutic strategies against viral infections and optimizing current treatment regimens. Detection, analysis and interpretation of drug resistance mutation methods has several challenges, including: a) presence of viral quasispecies, b) number of viral genotypes/serotypes/clades, c) overlapping coding regions, d) inconsistencies in amino acid numbering, e) identification and numbering of mutations in extremely variable regions such as envelope proteins, and f) precise identification of resistance mutations from a pool of mutations which include resistance mutations and natural polymorphisms. We have developed and implemented several bioinformatics solutions to address the majority of the issues listed above. The current poster/talk will discuss some of the bioinformatics issues and approaches in detection and analysis of drug resistance mutations with the main emphasis on development of a unique numbering convention, the Virtual Frame of Reference (VFR), which would address several of the challenges faced by the bioinformatics community in designing tools to aid in mutation detection, mutation selection and/or analysis of drug resistance.