A comparison of computational methods for identifying virulence factors.

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lu-Lu Zheng, Yi-Xue Li, Juan Ding, Xiao-Kui Guo, Kai-Yan Feng, Ya-Jun Wang, Le-Le Hu, Yu-Dong Cai, Pei Hao, Kuo-Chen Chou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/32f3339b8a80408e82ac0ccd21e0740f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:32f3339b8a80408e82ac0ccd21e0740f
record_format	dspace
spelling	oai:doaj.org-article:32f3339b8a80408e82ac0ccd21e0740f2021-11-18T07:09:44ZA comparison of computational methods for identifying virulence factors.1932-620310.1371/journal.pone.0042517https://doaj.org/article/32f3339b8a80408e82ac0ccd21e0740f2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22880014/?tool=EBIhttps://doaj.org/toc/1932-6203Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.Lu-Lu ZhengYi-Xue LiJuan DingXiao-Kui GuoKai-Yan FengYa-Jun WangLe-Le HuYu-Dong CaiPei HaoKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e42517 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lu-Lu Zheng Yi-Xue Li Juan Ding Xiao-Kui Guo Kai-Yan Feng Ya-Jun Wang Le-Le Hu Yu-Dong Cai Pei Hao Kuo-Chen Chou A comparison of computational methods for identifying virulence factors.
description	Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.
format	article
author	Lu-Lu Zheng Yi-Xue Li Juan Ding Xiao-Kui Guo Kai-Yan Feng Ya-Jun Wang Le-Le Hu Yu-Dong Cai Pei Hao Kuo-Chen Chou
author_facet	Lu-Lu Zheng Yi-Xue Li Juan Ding Xiao-Kui Guo Kai-Yan Feng Ya-Jun Wang Le-Le Hu Yu-Dong Cai Pei Hao Kuo-Chen Chou
author_sort	Lu-Lu Zheng
title	A comparison of computational methods for identifying virulence factors.
title_short	A comparison of computational methods for identifying virulence factors.
title_full	A comparison of computational methods for identifying virulence factors.
title_fullStr	A comparison of computational methods for identifying virulence factors.
title_full_unstemmed	A comparison of computational methods for identifying virulence factors.
title_sort	comparison of computational methods for identifying virulence factors.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/32f3339b8a80408e82ac0ccd21e0740f
work_keys_str_mv	AT luluzheng acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT yixueli acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT juanding acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT xiaokuiguo acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT kaiyanfeng acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT yajunwang acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT lelehu acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT yudongcai acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT peihao acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT kuochenchou acomparisonofcomputationalmethodsforidentifyingvirulencefactors AT luluzheng comparisonofcomputationalmethodsforidentifyingvirulencefactors AT yixueli comparisonofcomputationalmethodsforidentifyingvirulencefactors AT juanding comparisonofcomputationalmethodsforidentifyingvirulencefactors AT xiaokuiguo comparisonofcomputationalmethodsforidentifyingvirulencefactors AT kaiyanfeng comparisonofcomputationalmethodsforidentifyingvirulencefactors AT yajunwang comparisonofcomputationalmethodsforidentifyingvirulencefactors AT lelehu comparisonofcomputationalmethodsforidentifyingvirulencefactors AT yudongcai comparisonofcomputationalmethodsforidentifyingvirulencefactors AT peihao comparisonofcomputationalmethodsforidentifyingvirulencefactors AT kuochenchou comparisonofcomputationalmethodsforidentifyingvirulencefactors
_version_	1718423844194091008

A comparison of computational methods for identifying virulence factors.

Ejemplares similares