Geno2pheno: recombination detection for HIV-1 and HEV subtypes

Area of Science:

• Virology
• Computational Biology
• Genetics

Background:

• Antiretroviral therapy for HIV-1 has limitations, with therapy failure being a persistent challenge.
• Recombinant viral subtypes complicate treatment efficacy, necessitating improved diagnostic methods.
• Accurate identification of viral recombination is crucial for effective therapeutic strategies.

Purpose of the Study:

• To introduce g2p-recco, a novel computational tool for detecting de novo and known viral recombination events.
• To enhance the diagnosis of recombinant viral subtypes, including HIV-1 and HEV.
• To provide a tool independent of known circulating recombinant forms (CRFs).

Main Methods:

• Utilized a new implementation of the recco computational method for recombination detection.
• Employed mutation calls and probable breakpoints in multiple sequence alignments to identify recombination.
• Integrated g2p-recco into the geno2pheno web service for accessibility.
• Applied a tuning parameter to balance mutation call and breakpoint emphasis.

Main Results:

• Successfully detected novel and known recombinant events in HIV-1, independent of CRFs.
• Demonstrated the tool's applicability to other viruses, such as hepatitis E virus (HEV).
• Identified several previously unknown HEV recombinations.

Conclusions:

• g2p-recco offers a robust method for detecting diverse viral recombinations.
• The tool facilitates improved diagnosis, potentially leading to better therapeutic selections for HIV-1.
• The broad applicability of g2p-recco extends its utility to other viral pathogens like HEV.