GHTM

Global Health and Tropical Medicine

  • GHTM
    • About GHTM
    • Governance
    • Impact
    • Members
      • Population health, policies and services
        • PPS PhD members
        • PPS non PhD members
      • TB, HIV and opportunistic diseases and pathogens
        • THOP PhD members
        • THOP non PhD members
      • Vector-borne diseases
        • VBD PhD members
        • VBD non PhD members
      • Individual Health Care
        • IHC PhD members
        • IHC non PhD members
      • Tech & Admin support
    • Scientific Advisory Board
  • Research
    • Cross-cutting issues
      • Global Pathogen Dispersion and Population Mobility
      • Drug Discovery and Drug Resistance
      • Diagnostics
      • Public Health Information
      • Fair Research Partnerships
    • Research Groups
      • PPS – Population health, policies and services
      • THOP – TB, HIV and opportunistic diseases and pathogens
      • VBD – Vector borne diseases
      • IHC – Individual health care
    • Research in numbers
      • 2023
      • 2022
      • 2021
      • 2020
      • 2019
      • 2018
      • 2017
    • Projects
      • Ongoing Projects
      • Completed Projects
  • Outreach
    • Events
    • News
    • Policy Support & Community Outreach
  • Publications
    • 2024
    • 2023
    • 2022
    • 2021
    • 2020
    • 2019
    • 2018
    • 2017
    • 2016
    • 2015
  • Capacity Building
    • Education
      • Master Theses
      • PhD Theses
    • International
  • Infrastructures
  • Networks & Partnerships
  • Reports
    • GHTM
    • Scientific Advisory Board
    • FCT
Home / Archives for Deforche K

Genome Detective: an automated system for virus identification from high-throughput sequencing data

  • Authors: Vilsker M, Moosa Y, Nooij S, Fonseca V, Ghysens Y, Dumon K, Pauwels R, Alcantara LC, Vanden Eynden E, Vandamme AM, Deforche K, De Oliveira T
  • Publication Year: 2019
  • Journal: Bioinformatics
  • Link: https://www.ncbi.nlm.nih.gov/pubmed/30124794

Genome Detective is an easy to use web-based software application that assembles the genomes of viruses quickly and accurately. The application uses a novel alignment method that constructs genomes by reference-based linking of de novo contigs by combining amino-acids and nucleotide scores. The software was optimized using synthetic datasets to represent the great diversity of […]
Read More

A computational method for the identification of dengue, zika and chikungunya virus species and genotypes

  • Authors: Fonseca V, Libin PJK, Theys K, Faria NR, Nunes MRT, Restovic MI, Freire M, Giovanetti M, Cuypers L, Nowé A, Abecasis A, Deforche K, Santiago GA, Siqueira IC, San EJ, Machado KCB, Azevedo V, Filippis AMB, Cunha RVD, Pybus OG, Vandamme AM, Alcantara LCJ, De Oliveira T
  • Publication Year: 2019
  • Journal: Plos Neglected Tropical Diseases
  • Link: https://www.ncbi.nlm.nih.gov/pubmed/31067235

In recent years, an increasing number of outbreaks of Dengue, Chikungunya and Zika viruses have been reported in Asia and the Americas. Monitoring virus genotype diversity is crucial to understand the emergence and spread of outbreaks, both aspects that are vital to develop effective prevention and treatment strategies. Hence, we developed an efficient method to […]
Read More

A public HTLV-1 molecular epidemiology database for sequence management and data mining.

  • Authors: Alcantara LC, Araújo TH, de Albuquerque-Junior AE, Edwards D, Galvão-Castro B, Souza-Brito LI, Deforche K, Libin P, Vandamme AM
  • Journal: PLoS One
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/?term=A+Public+HTLV-1+Molecular+Epidemiology+Database+for+Sequence+Management+and+Data+Mining

BACKGROUND:
It is estimated that 15 to 20 million people are infected with the human T-cell lymphotropic virus type 1 (HTLV-1). At present, there are more than 2,000 unique HTLV-1 isolate sequences published. A central database to aggregate sequence information from a range of epidemiological aspects including HTLV-1 infections, pathogenesis, origins, and evolutionary dynamics would be useful to scientists and physicians worldwide.
Read More

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

  • Authors: Beheydt G, Bruzzone B, Camacho RJ, De Luca A, Deforche K, Grossman Z, Imbrechts S, Incardona F, Libin P, Pironti A, Rhee SY, Ruiz L, Sangeda RZ, Shafer RW, Sönnerborg A, Theys K, Torti C, Van de Vijver DA, Van Laethem K, Van Wijngaerden E, Vandamme AM, Vercauteren J, Zazzi M
  • Journal: Infection Genetics and Evolution
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/23523594

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment.
Read More

Automated subtyping of HIV-1 genetic sequences for clinical and surveillance purposes: Performance evaluation of the new REGA version 3 and seven other tools

  • Authors: Abecasis AB, Camacho RJ, De Oliveira T, Deforche K, Faria NR, Gomez-Lopez A, Imbrechts S, Libin P, Pineda-Peña AC, Vandamme AM
  • Journal: Infection Genetics and Evolution
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/23660484

To investigate differences in pathogenesis, diagnosis and resistance pathways between HIV-1 subtypes, an accurate subtyping tool for large datasets is needed. We aimed to evaluate the performance of automated subtyping tools to classify the different subtypes and circulating recombinant forms using pol, the most sequenced region in clinical practice. We also present the upgraded version 3 of the Rega HIV subtyping tool (REGAv3).
Read More

  • 1
  • 2
  • Next Page »

About GHTM

GHTM is a R&D Unit that brings together researchers with a track record in Tropical Medicine and International & Global Health. It aims at strengthening Portugal's role as a leading partner in the development and implementation of a global health research agenda. Our evidence-based interventions contribute to the promotion of equity in health and to improve the health of populations.

Contacts

Rua da Junqueira, 100
1349-008 Lisboa
Portugal

+351 213 652 600

  • E-mail
  • Facebook
  • LinkedIn
  • Twitter
  • YouTube

Map

  • Events
  • Research Groups
  • Cross-cutting issues
© Copyright 2025 IHMT-UNL All Rights Reserved.
  • Universidade Nova de Lisboa
  • Fundação para a Ciência e a Tecnologia

    UIDB/04413/2020
    UIDP/04413/2020

We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.Ok