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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Biruhalem Taye, Candida Vaz, Vivek Tanavde, Vladimir A. Kuznetsov, Frank Eisenhaber, Richard J. Sugrue, Sebastian Maurer-Stroh

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. More... »

PAGES

5805

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-06020-6

DOI

http://dx.doi.org/10.1038/s41598-017-06020-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090707292

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28724991


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