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GraphAlignment: Bayesian pairwise alignment of biological networks

Michal Kolář12, Jörn Meier1, Ville Mustonen13, Michael Lässig1 and Johannes Berg1*

Author Affiliations

1 Institut für Theoretische Physik, Universität zu Köln, Zülpicher Straße 77, D-50937 Köln, Germany

2 Institute of Molecular Genetics, Academy of Sciences of the Czech Republic, Vídeňská 1083, CZ-14220 Praha, Czech Republic

3 Present address: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK

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BMC Systems Biology 2012, 6:144  doi:10.1186/1752-0509-6-144

Published: 21 November 2012

Additional files

Additional file 1:

The Additional file 1 contains the codes used to generate the network instances and to find the optimal alignment byGraphAlignmentandGræmlin 2.0, Figures S1 and S2. Further, it contains Figure S3 with the matrix of vertex similarities Θ(i,i)and the matrix of correlations between the edge weights of vertices i in G and i in G for the scenarios (i) and (ii). Figures S4 and S5 give the computational complexity and accuracy of the GraphAlignment and Gæmlin algorithms in scenario (ia) with the edge weights drawn from the normal distribution. Finally, Table S1 compares the GraphAlignment and Græmlin performance on empirical gene co-expression networks.

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