Date |
Oct 7, 2013 |
Speaker |
Dr. Michal Kolar, Academy of Sciences of the Czech Republic, Prague
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Title |
Graph alignment of protein-protein interaction networks: From
interactions to functional annotation
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Abstract |
Sequence 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. We demonstrate how information on gene
interactions can help to resolve ambiguous sequence alignments and
introduce the Bioconductor package GraphAlignment for pairwise alignment
of bio-molecular networks. The GraphAlignment algorithm is based on an
explicit evolutionary model and allows inference of all scoring
parameters directly from empirical data. It is robust to spurious vertex
associations, correctly resolves paralogs, and shows very good
performance in identification of homologous vertices defined by high
vertex and/or interaction similarity. The simplicity and generality of
GraphAlignment scoring makes the algorithm an appropriate choice for
global alignment of networks.
As a proof of principle, we compare two distant Herpes viruses by
providing functional associations between proteins of the two organisms
that cannot be obtained from sequence or interaction data alone. We
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.
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