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.