ArticleBMC systems biology2008
From protein interactions to functional annotation: graph alignment in Herpes.
Article in BMC systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- NAIGO: An Improved Method to Align PPI Networks Based on Gene Ontology and Graphlets.Frontiers in bioengineering and biotechnology · 2020Article
- Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs.Computational and structural biotechnology journal · 2017Article
- BinAligner: a heuristic method to align biological networks.BMC bioinformatics · 2013Article
- GraphAlignment: Bayesian pairwise alignment of biological networks.BMC systems biology · 2012Article
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3 authors.
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Abstract
backgroundSequence alignment is a prolific basis of functional annotation, but remains a challenging problem in the 'twilight zone' of high sequence divergence or short gene length. Here we demonstrate how information on gene interactions can help to resolve ambiguous sequence alignments. We compare two distant Herpes viruses by constructing a graph alignment, which is based jointly on the similarity of their protein interaction networks and on sequence similarity. This hybrid method provides functional associations between proteins of the two organisms that cannot be obtained from sequence or interaction data alone.
resultsWe find proteins where interaction similarity and sequence similarity are individually weak, but together provide significant evidence of orthology. There are also proteins with high interaction similarity but without any detectable sequence similarity, providing evidence of functional association beyond sequence homology. The functional predictions derived from our alignment are consistent with genomic position and gene expression data.
conclusionOur approach shows that evolutionary conservation is a powerful filter to make protein interaction data informative about functional similarities between the interacting proteins, and it establishes graph alignment as a powerful tool for the comparative analysis of data from highly diverged species.
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