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A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria
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 in...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789135/ https://www.ncbi.nlm.nih.gov/pubmed/36564617 http://dx.doi.org/10.1038/s42003-022-04352-2 |
| _version_ | 1784858909929897984 |
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| author | Trimarsanto, Hidayat Amato, Roberto Pearson, Richard D. Sutanto, Edwin Noviyanti, Rintis Trianty, Leily Marfurt, Jutta Pava, Zuleima Echeverry, Diego F. Lopera-Mesa, Tatiana M. Montenegro, Lidia M. Tobón-Castaño, Alberto Grigg, Matthew J. Barber, Bridget William, Timothy Anstey, Nicholas M. Getachew, Sisay Petros, Beyene Aseffa, Abraham Assefa, Ashenafi Rahim, Awab G. Chau, Nguyen H. Hien, Tran T. Alam, Mohammad S. Khan, Wasif A. Ley, Benedikt Thriemer, Kamala Wangchuck, Sonam Hamedi, Yaghoob Adam, Ishag Liu, Yaobao Gao, Qi Sriprawat, Kanlaya Ferreira, Marcelo U. Laman, Moses Barry, Alyssa Mueller, Ivo Lacerda, Marcus V. G. Llanos-Cuentas, Alejandro Krudsood, Srivicha Lon, Chanthap Mohammed, Rezika Yilma, Daniel Pereira, Dhelio B. Espino, Fe E. J. Chu, Cindy S. Vélez, Iván D. Namaik-larp, Chayadol Villegas, Maria F. Green, Justin A. Koh, Gavin Rayner, Julian C. Drury, Eleanor Gonçalves, Sónia Simpson, Victoria Miotto, Olivo Miles, Alistair White, Nicholas J. Nosten, Francois Kwiatkowski, Dominic P. Price, Ric N. Auburn, Sarah |
| author_facet | Trimarsanto, Hidayat Amato, Roberto Pearson, Richard D. Sutanto, Edwin Noviyanti, Rintis Trianty, Leily Marfurt, Jutta Pava, Zuleima Echeverry, Diego F. Lopera-Mesa, Tatiana M. Montenegro, Lidia M. Tobón-Castaño, Alberto Grigg, Matthew J. Barber, Bridget William, Timothy Anstey, Nicholas M. Getachew, Sisay Petros, Beyene Aseffa, Abraham Assefa, Ashenafi Rahim, Awab G. Chau, Nguyen H. Hien, Tran T. Alam, Mohammad S. Khan, Wasif A. Ley, Benedikt Thriemer, Kamala Wangchuck, Sonam Hamedi, Yaghoob Adam, Ishag Liu, Yaobao Gao, Qi Sriprawat, Kanlaya Ferreira, Marcelo U. Laman, Moses Barry, Alyssa Mueller, Ivo Lacerda, Marcus V. G. Llanos-Cuentas, Alejandro Krudsood, Srivicha Lon, Chanthap Mohammed, Rezika Yilma, Daniel Pereira, Dhelio B. Espino, Fe E. J. Chu, Cindy S. Vélez, Iván D. Namaik-larp, Chayadol Villegas, Maria F. Green, Justin A. Koh, Gavin Rayner, Julian C. Drury, Eleanor Gonçalves, Sónia Simpson, Victoria Miotto, Olivo Miles, Alistair White, Nicholas J. Nosten, Francois Kwiatkowski, Dominic P. Price, Ric N. Auburn, Sarah |
| author_sort | Trimarsanto, Hidayat |
| collection | PubMed |
| description | 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. |
| format | Online Article Text |
| id | pubmed-9789135 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2022 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-97891352022-12-25 A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria Trimarsanto, Hidayat Amato, Roberto Pearson, Richard D. Sutanto, Edwin Noviyanti, Rintis Trianty, Leily Marfurt, Jutta Pava, Zuleima Echeverry, Diego F. Lopera-Mesa, Tatiana M. Montenegro, Lidia M. Tobón-Castaño, Alberto Grigg, Matthew J. Barber, Bridget William, Timothy Anstey, Nicholas M. Getachew, Sisay Petros, Beyene Aseffa, Abraham Assefa, Ashenafi Rahim, Awab G. Chau, Nguyen H. Hien, Tran T. Alam, Mohammad S. Khan, Wasif A. Ley, Benedikt Thriemer, Kamala Wangchuck, Sonam Hamedi, Yaghoob Adam, Ishag Liu, Yaobao Gao, Qi Sriprawat, Kanlaya Ferreira, Marcelo U. Laman, Moses Barry, Alyssa Mueller, Ivo Lacerda, Marcus V. G. Llanos-Cuentas, Alejandro Krudsood, Srivicha Lon, Chanthap Mohammed, Rezika Yilma, Daniel Pereira, Dhelio B. Espino, Fe E. J. Chu, Cindy S. Vélez, Iván D. Namaik-larp, Chayadol Villegas, Maria F. Green, Justin A. Koh, Gavin Rayner, Julian C. Drury, Eleanor Gonçalves, Sónia Simpson, Victoria Miotto, Olivo Miles, Alistair White, Nicholas J. Nosten, Francois Kwiatkowski, Dominic P. Price, Ric N. Auburn, Sarah Commun Biol Article 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. Nature Publishing Group UK 2022-12-23 /pmc/articles/PMC9789135/ /pubmed/36564617 http://dx.doi.org/10.1038/s42003-022-04352-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
| spellingShingle | Article Trimarsanto, Hidayat Amato, Roberto Pearson, Richard D. Sutanto, Edwin Noviyanti, Rintis Trianty, Leily Marfurt, Jutta Pava, Zuleima Echeverry, Diego F. Lopera-Mesa, Tatiana M. Montenegro, Lidia M. Tobón-Castaño, Alberto Grigg, Matthew J. Barber, Bridget William, Timothy Anstey, Nicholas M. Getachew, Sisay Petros, Beyene Aseffa, Abraham Assefa, Ashenafi Rahim, Awab G. Chau, Nguyen H. Hien, Tran T. Alam, Mohammad S. Khan, Wasif A. Ley, Benedikt Thriemer, Kamala Wangchuck, Sonam Hamedi, Yaghoob Adam, Ishag Liu, Yaobao Gao, Qi Sriprawat, Kanlaya Ferreira, Marcelo U. Laman, Moses Barry, Alyssa Mueller, Ivo Lacerda, Marcus V. G. Llanos-Cuentas, Alejandro Krudsood, Srivicha Lon, Chanthap Mohammed, Rezika Yilma, Daniel Pereira, Dhelio B. Espino, Fe E. J. Chu, Cindy S. Vélez, Iván D. Namaik-larp, Chayadol Villegas, Maria F. Green, Justin A. Koh, Gavin Rayner, Julian C. Drury, Eleanor Gonçalves, Sónia Simpson, Victoria Miotto, Olivo Miles, Alistair White, Nicholas J. Nosten, Francois Kwiatkowski, Dominic P. Price, Ric N. Auburn, Sarah A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria |
| title | A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria |
| title_full | A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria |
| title_fullStr | A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria |
| title_full_unstemmed | A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria |
| title_short | A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria |
| title_sort | molecular barcode and web-based data analysis tool to identify imported plasmodium vivax malaria |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789135/ https://www.ncbi.nlm.nih.gov/pubmed/36564617 http://dx.doi.org/10.1038/s42003-022-04352-2 |
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