A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria
Issued Date
2022-12-01
Resource Type
eISSN
23993642
Scopus ID
2-s2.0-85144637810
Pubmed ID
36564617
Journal Title
Communications Biology
Volume
5
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Communications Biology Vol.5 No.1 (2022)
Suggested Citation
Trimarsanto H., Amato R., Pearson R.D., Sutanto E., Noviyanti R., Trianty L., Marfurt J., Pava Z., Echeverry D.F., Lopera-Mesa T.M., Montenegro L.M., Tobón-Castaño A., Grigg M.J., Barber B., William T., Anstey N.M., Getachew S., Petros B., Aseffa A., Assefa A., Rahim A.G., Chau N.H., Hien T.T., Alam M.S., Khan W.A., Ley B., Thriemer K., Wangchuck S., Hamedi Y., Adam I., Liu Y., Gao Q., Sriprawat K., Ferreira M.U., Laman M., Barry A., Mueller I., Lacerda M.V.G., Llanos-Cuentas A., Krudsood S., Lon C., Mohammed R., Yilma D., Pereira D.B., Espino F.E.J., Chu C.S., Vélez I.D., Namaik-larp C., Villegas M.F., Green J.A., Koh G., Rayner J.C., Drury E., Gonçalves S., Simpson V., Miotto O., Miles A., White N.J., Nosten F., Kwiatkowski D.P., Price R.N., Auburn S. A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria. Communications Biology Vol.5 No.1 (2022). doi:10.1038/s42003-022-04352-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/83499
Title
A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria
Author(s)
Trimarsanto H.
Amato R.
Pearson R.D.
Sutanto E.
Noviyanti R.
Trianty L.
Marfurt J.
Pava Z.
Echeverry D.F.
Lopera-Mesa T.M.
Montenegro L.M.
Tobón-Castaño A.
Grigg M.J.
Barber B.
William T.
Anstey N.M.
Getachew S.
Petros B.
Aseffa A.
Assefa A.
Rahim A.G.
Chau N.H.
Hien T.T.
Alam M.S.
Khan W.A.
Ley B.
Thriemer K.
Wangchuck S.
Hamedi Y.
Adam I.
Liu Y.
Gao Q.
Sriprawat K.
Ferreira M.U.
Laman M.
Barry A.
Mueller I.
Lacerda M.V.G.
Llanos-Cuentas A.
Krudsood S.
Lon C.
Mohammed R.
Yilma D.
Pereira D.B.
Espino F.E.J.
Chu C.S.
Vélez I.D.
Namaik-larp C.
Villegas M.F.
Green J.A.
Koh G.
Rayner J.C.
Drury E.
Gonçalves S.
Simpson V.
Miotto O.
Miles A.
White N.J.
Nosten F.
Kwiatkowski D.P.
Price R.N.
Auburn S.
Amato R.
Pearson R.D.
Sutanto E.
Noviyanti R.
Trianty L.
Marfurt J.
Pava Z.
Echeverry D.F.
Lopera-Mesa T.M.
Montenegro L.M.
Tobón-Castaño A.
Grigg M.J.
Barber B.
William T.
Anstey N.M.
Getachew S.
Petros B.
Aseffa A.
Assefa A.
Rahim A.G.
Chau N.H.
Hien T.T.
Alam M.S.
Khan W.A.
Ley B.
Thriemer K.
Wangchuck S.
Hamedi Y.
Adam I.
Liu Y.
Gao Q.
Sriprawat K.
Ferreira M.U.
Laman M.
Barry A.
Mueller I.
Lacerda M.V.G.
Llanos-Cuentas A.
Krudsood S.
Lon C.
Mohammed R.
Yilma D.
Pereira D.B.
Espino F.E.J.
Chu C.S.
Vélez I.D.
Namaik-larp C.
Villegas M.F.
Green J.A.
Koh G.
Rayner J.C.
Drury E.
Gonçalves S.
Simpson V.
Miotto O.
Miles A.
White N.J.
Nosten F.
Kwiatkowski D.P.
Price R.N.
Auburn S.
Author's Affiliation
Faculty of Tropical Medicine, Mahidol University
Ethiopian Public Health Institute
Oxford University Clinical Research Unit
Universidad Icesi
Cambridge Institute for Medical Research
University of Gondar
Jiangsu Institute of Parasitic Diseases
Papua New Guinea Institute of Medical Research
University of Khartoum Faculty of Medicine
Gokila
Universidad Peruana Cayetano Heredia
Jimma University
Armauer Hansen Research Institute
Addis Ababa University
Fundacao de Medicina Tropical do Amazonas
Eijkman Institute for Molecular Biology
Universidad del Valle, Cali
Instituto de Higiene e Medicina Tropical
Universidad de Antioquia
Centro Internacional de Entrenamiento e Investigaciones Medicas
Walter and Eliza Hall Institute of Medical Research
University of Melbourne
Fundacao Oswaldo Cruz
Menzies School of Health Research
GlaxoSmithKline plc.
Deakin University
Nanjing Medical University
Armed Forces Research Institute of Medical Sciences, Thailand
Mahidol University
International Centre for Diarrhoeal Disease Research Bangladesh
Nuffield Department of Medicine
Universidade de São Paulo
Wellcome Sanger Institute
Institut Pasteur, Paris
Hormozgan University of Medical Sciences
Exeins Health Initiative
Nangarhar University
Ministry of Health
Centro de Investigaciones Clínicas
Infectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit
Umphang Hospital
Queen Elizabeth Hospital
Centro de Pesquisa em Medicina Tropical
Ethiopian Public Health Institute
Oxford University Clinical Research Unit
Universidad Icesi
Cambridge Institute for Medical Research
University of Gondar
Jiangsu Institute of Parasitic Diseases
Papua New Guinea Institute of Medical Research
University of Khartoum Faculty of Medicine
Gokila
Universidad Peruana Cayetano Heredia
Jimma University
Armauer Hansen Research Institute
Addis Ababa University
Fundacao de Medicina Tropical do Amazonas
Eijkman Institute for Molecular Biology
Universidad del Valle, Cali
Instituto de Higiene e Medicina Tropical
Universidad de Antioquia
Centro Internacional de Entrenamiento e Investigaciones Medicas
Walter and Eliza Hall Institute of Medical Research
University of Melbourne
Fundacao Oswaldo Cruz
Menzies School of Health Research
GlaxoSmithKline plc.
Deakin University
Nanjing Medical University
Armed Forces Research Institute of Medical Sciences, Thailand
Mahidol University
International Centre for Diarrhoeal Disease Research Bangladesh
Nuffield Department of Medicine
Universidade de São Paulo
Wellcome Sanger Institute
Institut Pasteur, Paris
Hormozgan University of Medical Sciences
Exeins Health Initiative
Nangarhar University
Ministry of Health
Centro de Investigaciones Clínicas
Infectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit
Umphang Hospital
Queen Elizabeth Hospital
Centro de Pesquisa em Medicina Tropical
Other Contributor(s)
Abstract
Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection’s country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.