Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Improvement in the determination of HIV-1 tropism using the V3 gene sequence and a combination of bioinformatic tools.

Journal of medical virology - 1 May 2009

Chueca Natalia, Garrido Carolina, Alvarez Marta, Poveda Eva, de Dios Luna Juan, Zahonero Natalia, Hernández-Quero José, Soriano Vicente, Maroto Carmen, de Mendoza Carmen, García Federico

Abstract excerpt

Assessment of HIV tropism using bioinformatic tools based on V3 sequences correlates poorly with results provided by phenotypic tropism assays, particularly for recognizing X4 viruses. This may represent an obstacle for the use of CCR5 antagonists. An algorithm combining several bioinformatic tools might improve the correlation with phenotypic tropism results. A total of 200 V3 sequences from HIV-1 subtype B,...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to HIV-1

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to HIV Envelope Protein gp120

Share to Peptide Fragments

Share to Receptors, CCR5

Share to Receptors, CXCR4

Share to Sensitivity and Specificity

Share to Tropism

Source

Identifiers

PMID: 19319937
DOI: 10.1002/jmv.21425

Genes

CCR5

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Improvement in the determination of HIV-1 tropism using the V3 gene sequence and a combination of bioinformatic tools.PMID 19319937Read ↗

Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes.Semantic proximity Read ↗

V3 net charge: additional tool in HIV-1 tropism prediction.Semantic proximity Read ↗

Performance of a population-based HIV-1 tropism phenotypic assay and correlation with V3 genotypic prediction tools in recent HIV-1 seroconverters.Semantic proximity Read ↗

A highly sensitive and specific model for predicting HIV-1 tropism in treatment-experienced patients combining interpretation of V3 loop sequences and clinical parameters.Semantic proximity Read ↗

Correlation between genotypic predictions based on V3 sequences and phenotypic determination of HIV-1 tropism.Semantic proximity Read ↗

Comparison of two genotypic algorithms to determine HIV-1 tropism.Semantic proximity Read ↗

HIV-V3Augur: A Novel Machine Learning Model for Predicting HIV-1 Tropism in Sub-Subtype A6 and CRF63_02A6, Predominant Variants in Russia and Countries of the Former Soviet Union.Semantic proximity Read ↗

Performance of genotypic tools for prediction of tropism in HIV-1 subtype C V3 loop sequences.Semantic proximity Read ↗

Design and validation of new genotypic tools for easy and reliable estimation of HIV tropism before using CCR5 antagonists.Semantic proximity Read ↗

Performance of genotypic algorithms for predicting tropism of HIV-1CRF02_AG subtype.Semantic proximity Read ↗

Performance of genotypic algorithms for predicting tropism for HIV-1 CRF01_AE recombinant.Semantic proximity Read ↗

Comparison of population and 454 "deep" sequence analysis for HIV type 1 tropism versus the original trofile assay in non-B subtypes.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.