Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data
Data(s) |
2015
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Resumo |
Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data. |
Formato |
application/pdf application/pdf |
Identificador | |
Publicador |
Elsevier |
Relação |
http://eprints.qut.edu.au/83576/1/83576_IJMM_Final.pdf http://eprints.qut.edu.au/83576/2/83576_IJMM_supp_info_Final.pdf DOI:10.1016/j.ijmm.2015.02.001 Broadbent, J.A., Sampson, D.L., Broszczak, D.A., Upton, Z., & Huygens, F. (2015) Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data. International Journal of Medical Microbiology, 305(3), pp. 339-347. QUT/IHBI ECR Grant |
Direitos |
Copyright 2015 Elsevier Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International. DOI: 10.1016/j.ijmm.2015.02.001 |
Fonte |
School of Biomedical Sciences; Faculty of Health; Institute of Health and Biomedical Innovation |
Palavras-Chave | #060101 Analytical Biochemistry #060102 Bioinformatics #060501 Bacteriology #Staphylococcus aureus #Omics #Systems biology #Gene ontology #Molecular Network |
Tipo |
Journal Article |