NetworkBLAST: comparative analysis of protein networks

Abstract

Summary: The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Crossspecies analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks.

Aligning Biomolecular Networks Using Modular Graph Kernels

“…In each case, both modified shortest path and modified random walk graph kernels were used to compute similarity between pairs of subgraphs. We compare the performance of the resulting algorithms with variants of NetworkBLAST [24] and HopeMap [44], which are among the state-of-the-art algorithms for pair-wise alignment of protein-protein interaction networks, using metrics proposed by Kalaev et al [24]. The NetworkBLAST algorithm uses BLASTp [2] to match the nodes across the different networks being aligned, whereas HopeMap uses InParanoid [34] orthology groups to match the nodes across different networks.…”

“…Our experiments with the fly, yeast, mouse and human protein-protein interaction networks extracted from DIP (Database of Interacting Proteins) [38] demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with variants of the NetworkBLAST and HopeMap, which are among the stateof-the-art algorithms for pair-wise alignment of large protein-protein interaction networks [24,44].…”

“…To evaluate the alignments, Kalaev et al's approach was implemented as described in the NetworkBLAST [24] and the HopeMap [44] papers. Recall from section 2.1 that the output of the alignment algorithm is a set of subgraphs S 1 and S 2 (corresponding to the query and target networks, respectively).…”

“…The results in table 1 show a comparison of the performance of the k -hop neighborhood based alignment with variants of NetworkBLAST [24] and HopeMap [44].…”

SubMAP: Aligning Metabolic Pathways with Subnetwork Mappings

“…The first type considers one pathway at a time and explores its important properties such as its robustness (Edwards and Palsson, 2000), steady states (Devloo et al, 2003;Garg et al, 2007;Ay et al, 2009b), modular structure (Lu et al, 2006;Schuster et al, 2002;Ay et al, 2010), network motifs (Milo et al, 298;Wernicke and Rasche, 2006;Grochow and Kellis, 2007), as well as its representation (Michal, 1998;Babur et al, 2010). The second type is the comparative approach which considers multiple pathways to identify their frequent subgraphs (Koyuturk et al, 2004;Qian and Yoon, 2009) and their alignments (Singh et al, 2008(Singh et al, , 2007Liao et al, 2009;Kalaev et al, 2008;Sharan et al, 2005;Kalaev et al, 2009;Dost et al, 2007;Flannick et al, 2006;Koyuturk et al, 2005;Pinter et al, 2005;Berg and Lassig, 2004;Dandekar et al, 1999;Tohsato and Nishimura, 2008;Tohsato et al, 2000;Cheng et al, 2009;Ay et al, 2008Ay et al, , 2009a. Alignment is a fundamental type of comparative analysis that aims to identify similar parts between pathways.…”