Benchmarking selected computational gene network growing tools in context of virus-host interactions

Abstract Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions,...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Biruhalem Taye, Candida Vaz, Vivek Tanavde, Vladimir A. Kuznetsov, Frank Eisenhaber, Richard J. Sugrue, Sebastian Maurer-Stroh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0e254567aa94483c8cfe264b6e1e0cf0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0e254567aa94483c8cfe264b6e1e0cf0
record_format	dspace
spelling	oai:doaj.org-article:0e254567aa94483c8cfe264b6e1e0cf02021-12-02T11:40:33ZBenchmarking selected computational gene network growing tools in context of virus-host interactions10.1038/s41598-017-06020-62045-2322https://doaj.org/article/0e254567aa94483c8cfe264b6e1e0cf02017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06020-6https://doaj.org/toc/2045-2322Abstract Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (~30 genes) or medium (~150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes.Biruhalem TayeCandida VazVivek TanavdeVladimir A. KuznetsovFrank EisenhaberRichard J. SugrueSebastian Maurer-StrohNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Biruhalem Taye Candida Vaz Vivek Tanavde Vladimir A. Kuznetsov Frank Eisenhaber Richard J. Sugrue Sebastian Maurer-Stroh Benchmarking selected computational gene network growing tools in context of virus-host interactions
description	Abstract Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (~30 genes) or medium (~150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes.
format	article
author	Biruhalem Taye Candida Vaz Vivek Tanavde Vladimir A. Kuznetsov Frank Eisenhaber Richard J. Sugrue Sebastian Maurer-Stroh
author_facet	Biruhalem Taye Candida Vaz Vivek Tanavde Vladimir A. Kuznetsov Frank Eisenhaber Richard J. Sugrue Sebastian Maurer-Stroh
author_sort	Biruhalem Taye
title	Benchmarking selected computational gene network growing tools in context of virus-host interactions
title_short	Benchmarking selected computational gene network growing tools in context of virus-host interactions
title_full	Benchmarking selected computational gene network growing tools in context of virus-host interactions
title_fullStr	Benchmarking selected computational gene network growing tools in context of virus-host interactions
title_full_unstemmed	Benchmarking selected computational gene network growing tools in context of virus-host interactions
title_sort	benchmarking selected computational gene network growing tools in context of virus-host interactions
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/0e254567aa94483c8cfe264b6e1e0cf0
work_keys_str_mv	AT biruhalemtaye benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions AT candidavaz benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions AT vivektanavde benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions AT vladimirakuznetsov benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions AT frankeisenhaber benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions AT richardjsugrue benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions AT sebastianmaurerstroh benchmarkingselectedcomputationalgenenetworkgrowingtoolsincontextofvirushostinteractions
_version_	1718395561594322944

Benchmarking selected computational gene network growing tools in context of virus-host interactions

Ejemplares similares