HIVIntact

HIVIntact infers the genetic intactness of HIV-1 proviral genomes to identify potentially replication-competent integrated proviruses for characterization of the HIV-1 reservoir.


Key Features:

  • Automated Characterization: Automates genetic characterization and annotation of near full-length (NFL) HIV-1 sequences, detecting insertions, deletions, and substitutions that disrupt coding regions.
  • Integration into Pipelines: Integrates with deep-sequencing and next-generation sequencing pipelines to process large datasets derived from near full-length PCR and sequencing.
  • Identification of Intact Genomes: Classifies proviral sequences as genetically intact or defective based on detected genomic defects, flagging sequences likely to be replication competent.
  • Customizable Settings: Provides adjustable analytic parameters to tailor defect-calling and annotation criteria to specific study designs.

Scientific Applications:

  • Reservoir Characterization: Quantifies and catalogs intact versus defective HIV-1 proviruses to inform studies of reservoir size and composition during antiretroviral therapy (ART).
  • Cure Strategy Research: Supports evaluation of interventions aimed at eliminating or controlling the reservoir by identifying sequences likely capable of replication.

Methodology:

Processes near full-length PCR and sequencing data to annotate HIV-1 sequences, detect insertions/deletions/substitutions, assess whether proviruses carry defects, and classify sequences as intact or defective while integrating with deep-sequencing/next-generation sequencing pipelines.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
workflow
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
11/11/2021
Last Updated:
11/11/2021

Operations

Publications

Wright IA, Bale MJ, Shao W, Hu W, Coffin JM, Van Zyl GU, Kearney MF. HIVIntact: a python-based tool for HIV-1 genome intactness inference. Retrovirology. 2021;18(1). doi:10.1186/s12977-021-00561-5. PMID:34176496. PMCID:PMC8237426.

PMID: 34176496
PMCID: PMC8237426
Funding: - National Cancer Institute: U01CA200441