SieveSifter

SieveSifter visualizes and analyzes sieve analyses from HIV-1 vaccine efficacy trials to identify viral genetic signatures and vaccine-induced selective pressures associated with partial protection.


Key Features:

  • Visualization: Visualizes sieve analyses and genetic sequence comparisons derived from HIV-1 vaccine efficacy trials.
  • Aggregated sequence data: Aggregates and organizes viral genetic sequence data from the HVTN 502/Step, RV144/Thai, HVTN 503/Phambili, and HVTN 505 trials for comparative analysis.
  • Rapid reinterpretation: Enables reanalysis of sieve effects across trial datasets to support hypothesis testing and comparative studies.
  • Methodology development support: Provides comprehensive trial-derived datasets to support development and benchmarking of novel sieve analysis methodologies.

Scientific Applications:

  • Elucidation of immune specificities: Compare sequences from vaccine and placebo recipients against the vaccine sequence to identify functional specificities of vaccine-induced immune responses.
  • Detection of correlates: Identify viral sequence characteristics associated with partial protection and vaccine-associated selection.
  • Vaccine design guidance: Inform HIV-1 vaccine antigen selection and immunogen design by revealing molecular interactions between vaccine-elicited responses and viral variants.

Methodology:

Performs sieve analysis on genetic sequence data from the HVTN 502/Step, RV144/Thai, HVTN 503/Phambili, and HVTN 505 trials to identify sequence differences associated with vaccine-induced immune pressure.

Topics

Details

License:
Unlicense
Maturity:
Mature
Cost:
Free of charge
Tool Type:
api, web application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
JavaScript
Added:
7/8/2019
Last Updated:
6/16/2020

Operations

Publications

Fiore-Gartland A, Kullman N, deCamp AC, Clenaghan G, Yang W, Magaret CA, Edlefsen PT, Gilbert PB. SieveSifter: a web-based tool for visualizing the sieve analyses of HIV-1 vaccine efficacy trials. Bioinformatics. 2017;33(15):2386-2388. doi:10.1093/bioinformatics/btx168. PMID:28379332. PMCID:PMC5860116.

Funding: - National Institutes of Health: R37AI054165, UM1AI068618, UM1AI068635

Documentation

Links