Functionally guided alignment of protein interaction networks for module detection.
BACKGROUND
Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.


RESULTS
Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.


BACKGROUND
Program binaries and source code is freely available at http://www.stats.ox.ac.uk/research/bioinfo/resources.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Statistics, University of Oxford, OX1 3TG, UK. ali@stats.ox.ac.uk
Waqar
W
Ali
Charlotte M
CM
Deane
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods
D011506:Proteins