ゲノム情報科学研究教育機構  アブストラクト
Date Oct 7, 2013
Speaker Dr. Michal Kolar, Academy of Sciences of the Czech Republic, Prague
Title Graph alignment of protein-protein interaction networks: From interactions to functional annotation
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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