W110 Nucleic Acids Research, 2009, Vol. 37, Web Server issue Figure 1. Schema of the analysis steps implemented in SNOW. Two complementary analyses are carried out: (1) the distribution of parameters (connection degree, betweenness centrality, clustering coeﬃcient and number of components) of the list of proteins are tested against the rest of proteins of the interactome of reference within the context of connections of the complete interactome of reference and (2) The list of proteins is mapped onto the reference interactome and the MCN is found. The distributions of networks parameters in this MCN are tested against their random expectation (see ‘Testing strategy for the network parameters’ section in the Supplementary Data). See Supplementary Figure 2 for a more detailed schema. Alternatively two lists of proteins can be compared by testing for signiﬁcant deviations in their respective para- meters’ distributions. Figure 1 shows a schema of the ana- lysis steps implemented in SNOW. Input SNOW inputs a collection of protein (or gene) identiﬁers in plain text. Most of the standard protein and gene iden- tiﬁers are accepted given that the tool uses the database of identiﬁers of Babelomics (23). Alternatively, a user-deﬁned interactome can be pro- vided to SNOW. It can be uploaded either as a list of protein–protein (or gene–gene) interactions tab-delimited in plain text or in the popular SIF format, used by Cytoscape (see http://www.cytoscape.org/cgi-bin/ moin.cgi/Cytoscape_User_Manual/Network_Formats). Calculation of network parameters and statistics In particular, SNOW tests four topological parameters: (i) Node connections degree, which was computed as the number of edges (interaction events) for a node; (ii) Betweenness, which depends on the number of shortest pathways passing through a given node; (iii) Clustering coeﬃcient, which measures the connectivity of the neigh- bourhood and (iv) Number of components of the network (see Additional methods for a detailed description on the calculation of these parameters). SNOW also ﬁnds the number of bicomponents and the articulation points and gives detailed descriptions of them. The parameters calculated account for diﬀerent network properties. For example, signalling networks tend to have high connectiv- ity and low clustering coeﬃcient while metabolic networks have higher clustering coeﬃcients. The three ﬁrst parameters (degree, betweenness and clustering coeﬃcient) are calculated for all the nodes in the network. Thus, a distribution of values for any of these parameters can be derived for each particular net- work, which can give information on the network proper- ties. Consequently, the comparison of two networks by contrasting how diﬀerent they are in terms of their char- acteristic parameters is straightforward by means of a Kolmogorov–Smirnov test. The potential biological rele- vance of a particular network can therefore be obtained by comparing the network parameter distributions to the corresponding empirical distributions derived from ten thousand networks with the same number of nodes and a random protein composition (see ‘Testing strategy for the network parameters’ section in the Supplementary Data). By default the network is compared to the inter- actome of reference and to a network of random composi- tion. However, the direct comparison of two networks is also possible. In addition, the same analysis can be performed by adding one (or two or even three) intermediate nodes