Name
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. 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.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.Our 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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1186/1752-0509-2-90 | BMC systems biology |
Keywords | Field | DocType |
protein binding,algorithms,bioinformatics,open reading frames,open reading frame,comparative analysis,evolutionary conservation,sequence alignment,systems biology,quantitative method,call graph | Sequence alignment,Gene,Annotation,Alignment-free sequence analysis,Biology,Systems biology,Bioinformatics,Multiple sequence alignment,Consensus sequence,Virion assembly | Journal |
Volume | Issue | ISSN |
2 | 1 | 1752-0509 |
Citations | PageRank | References |
10 | 0.57 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Kolář | 1 | 14 | 0.95 |
| Michael Lässig | 2 | 67 | 11.14 |
| Johannes Berg | 3 | 41 | 6.32 |