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HIVIntact: a python-based tool for HIV-1 genome intactness inference.

Imogen A Wright1, Michael J Bale2,3, Wei Shao4

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Identifying intact HIV-1 proviruses is crucial for a functional cure. We developed HIVIntact, a python tool for automated in-silico characterization of HIV-1 genomic defects in near full-length sequences.

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Area of Science:

  • Virology
  • Genomics
  • Bioinformatics

Background:

  • The HIV-1 reservoir comprises replication-competent proviruses persisting during antiretroviral therapy (ART).
  • Distinguishing intact from defective proviruses is challenging due to the rarity of intact forms.
  • Accurate characterization is vital for developing functional cure strategies.

Purpose of the Study:

  • To develop an automated computational tool for characterizing genomic defects in near full-length (NFL) HIV-1 sequences.
  • To enable in-silico identification of potentially replication-competent HIV-1 proviruses.
  • To facilitate the integration of HIV genome analysis into deep-sequencing pipelines.

Main Methods:

  • Development of HIVIntact, a Python-based software tool.
  • Utilizes near full-length (NFL) PCR and sequencing data.
  • Automated characterization and annotation of HIV-1 genomic defects.

Main Results:

  • HIVIntact accurately identifies genomic defects in NFL HIV-1 sequences.
  • Enables in-silico classification of putative intact proviruses.
  • Designed for seamless integration into existing sequence analysis pipelines.

Conclusions:

  • HIVIntact offers an automated, adaptable solution for analyzing HIV-1 genomic intactness.
  • Facilitates large-scale next-generation sequencing data analysis for HIV reservoir characterization.
  • A significant advancement for research towards a functional cure for HIV-1.