"Master-Slave" Biological Network Alignment

Performing global alignment between protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform this task operate symmetrically, that is to say, they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how well the corresponding organism is biologically well-characterized. For well-characterized organisms the associated PPI network supposedly encode in a sound manner all the information about their proteins and associated interactions, which is far from being the case for not well characterized ones. Here the new idea is developed to devise a method for global alignment of PPI networks that in fact exploit differences in the characterization of organisms at hand. We assume that the PPI network (called Master) of the best characterized is used as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master (and using the Slave) network. We tested our method showing that the results it returns are biologically relevant.

[1]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[2]  Sean R. Collins,et al.  Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.

[3]  Bonnie Berger,et al.  Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology , 2007, RECOMB.

[4]  Gunnar W. Klau,et al.  A new graph-based method for pairwise global network alignment , 2009, BMC Bioinformatics.

[5]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[6]  Serafim Batzoglou,et al.  Automatic Parameter Learning for Multiple Network Alignment , 2008, RECOMB.

[7]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[8]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[9]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[10]  Bonnie Berger,et al.  IsoRankN: spectral methods for global alignment of multiple protein networks , 2009, Bioinform..

[11]  Richard M. Karp,et al.  Comparing Protein Interaction Networks via a Graph Match-and-Split Algorithm , 2007, J. Comput. Biol..

[12]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[13]  André Kempe.  Viterbi Algorithm Generalized for n-Tape Best-Path Search , 2006, ArXiv.

[14]  Bonnie Berger,et al.  Global Alignment of Multiple Protein Interaction Networks , 2008, Pacific Symposium on Biocomputing.

[15]  Roded Sharan,et al.  Fast and Accurate Alignment of Multiple Protein Networks , 2009, J. Comput. Biol..

[16]  David S. Johnson,et al.  Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran , 1979 .

[17]  Gianni Cesareni,et al.  WI‐PHI: A weighted yeast interactome enriched for direct physical interactions , 2007, Proteomics.

[18]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[19]  Stephen E. Stein,et al.  The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation , 2009, BMC Bioinformatics.