Title
GraphAlignment: Bayesian pairwise alignment of biological networks.
Abstract
With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks.We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0.On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6).On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment.The GraphAlignment algorithm 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 edge scoring makes the algorithm an appropriate choice for global alignment of networks.
Year
DOI
Venue
2012
10.1186/1752-0509-6-144
BMC systems biology
Keywords
Field
DocType
bioinformatics,algorithms,bayes theorem,systems biology,computer graphics,computational biology
Pairwise comparison,Biological network,Computer science,Bioconductor,Systems biology,Estimation theory,Bioinformatics,Computer graphics,Bayes' theorem,Bayesian probability
Journal
Volume
Issue
ISSN
6
1
1752-0509
Citations 
PageRank 
References 
4
0.38
28
Authors
5
Name
Order
Citations
PageRank
Michal Kolář1140.95
Jörn Meier240.38
Ville Mustonen372.40
Michael Lässig46711.14
Johannes Berg5416.32