(proteins originally not included in the list that can actu- ally link two or more proteins of the list), which is useful in proteomics analysis where often not all of the proteins can be identiﬁed in an experiment. Output The program outputs the average parameter values, their signiﬁcance and boxplots representing the comparison of their actual distributions in the network studied to the complete interactome (top boxplots Figure 2) and to a random network of the same size (bottom boxplots, Figure 2). SNOW outputs the number of components, bicomponents and articulation points. The program also provides exhaustive information on these parameters as well as functional information on the proteins in the list. Moreover, information about the shortest paths and articulation points is also available. A low resolution viewer of the network is present in the main results web page (Figure 2). This viewer can be opened in a new window (see below). SNOW includes 12 examples in which diﬀerent sub-networks along with their correspond- ing parameters can be visualized (see Tutorial link in the main menu of the SNOW program). A particularly inter- esting example can be found in the additional case study information. SNOW was used to analyze 168 genes induced by the over-expression of BRCA1, one of the most studied cancer-related genes. The application of con- ventional functional enrichment analysis (24) failed to detect any functionality signiﬁcantly over-represented among the genes. Nevertheless, SNOW detected a signif- icant concentration of highly connected (P < 0.0001) and central (P = 0.0017) proteins in the resulting MCN (see Additional Case Study for details). Visualization An interactive viewer for the connections deﬁning the network studied can also be opened from the results page. All the components of the network are displayed in diﬀerent layouts. The network orientation can be inter- actively changed. Gene names can be displayed and con- necting proteins (intermediate nodes) can be included or excluded from the graph. Supplementary Figure 1 shows two possible layouts of the same network obtained from cell-cycle dependent genes regulated following exposure to serum in a variety of human ﬁbroblast cell lines (data available from the third example included in the Tutorial and examples link of the main page of the SNOW program, see section above). The topmost view shows the menu with the options and the functional (gene ontology) and topological information on any pro- tein which is displayed when placing the mouse over it. Databases Human PPI datasets were downloaded from ﬁve main public databases: HPRD (25), IntAct (26), BIND (27), DIP (28) and MINT (29). We used this collection of PPI data to generate two scaﬀold interactomes: a non-ﬁltered scaﬀold interactome, which includes all the available PPIs; and a ﬁltered, more conﬁdent scaﬀold interactome. The six top categories of experimental methods described Nucleic Acids Research, 2009, Vol. 37, Web Server issue W111 in the Molecular Interaction (MI) Ontology (30) plus the categories in vivo and in vitro from HPRD can be used as conﬁdence indicators. Thus, only PPIs veriﬁed by at least two of these categories were considered in the ﬁltered scaﬀold interactome. Protein identiﬁers can be directly mapped to Ensembl transcript identiﬁers that can easily be linked to many other gene or protein identiﬁers. Since in many genomic experiments only data on genes (but not on particular transcripts) is available, we build up another two high and low conﬁdence interactomes by mapping transcripts onto genes. Conceptually, each gene can poten- tially interact with all the interaction partners of all of its transcripts. Transcript- and gene-based interactomes are similar but not identical. Implementation details The SNOW web interface was implemented using Perl and JavaScript languages. The queries to the server are imple- mented as web services written in Perl and Pyhton. Boost Graph libraries (http://www.boost.org/libs/graph/doc/ index.html) were used to perform graph parameters calculation. SNOW accesses these libraries through BGL-Python bindings (http://www.osl.iu.edu/ dgregor/ bgl-python/). SNOW uses R programming language to perform the statistics and mysql to store interactome network parameters. The visualization applet was imple- mented in Java using TouchGraph libraries (http:// touchgraph.sourceforge.net). Other tools There are several popular stand-alone tools for the visu- alization and analysis of networks, such as the popular Cytoscape (13,14) and other analogous tools (15,16). Also, in the last years, several web-based applications for this purpose have been reported in the literature (17–19,31). Some tools provide annotations (32), oﬀer functional enrichment analysis (32,33) or are more focused to pathways (19,34,35) or provides some facilities for net- work comparisons (20). Several databases oﬀer their own network viewers as is the case of STRING (36) or IntAct (26). Other tools allow for the calculation of some net- work parameters (20–22,37), although in some cases at the expense of losing interactivity in the visualization of the network (22). Supplementary Table 1 contains a list of web tools for network management with some of their most signiﬁcant features. DISCUSSION SNOW is unique among its genre in the sense that it not only allows visualizing data in an interactive dynamic format while calculating relevant network parameters but it also ﬁnds their statistical signiﬁcance. It can also be used to compare two networks in terms of their corre- sponding distributions of parameters. To our knowledge there are no other web-based tools with such features. SNOW constitutes a step forward in our eﬀorts to pro- vide the scientiﬁc community with web tools, such as the Babelomics suite (38), for the functional proﬁling of geno- mic experiments. SNOW broadens the conventional