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As said before, SNOW performs two different and complementary types of analysis to the list of proteins/genes submitted:

  1. Evaluates role of the list within the interactome. Snow identifies hubs in the list of proteins/genes (nodes) and evaluates the global degree of connections, centrality and clustering by comparing the distributions of nodes of the list versus the complete distribution of these parameters into the interactome.
  2. Evaluates the list’s cooperative behaviour as a functional module. SNOW calculates the MCN, the minimum network that connects the proteins/genes in the list using or without using an external nodes (a non-listed protein) to connect nodes in the list. The topology of this network is evaluated by comparing distributions of node parameters of this MCN against a set of random MCNs with same size range. This approach is similar to other’s tools for functional enrichment analysis such as FatiGO or Marmite with the difference of not having pre-annotated functional modules to evaluate, instead SNOW have to build it, that is the MCN.

There are also two more ways of usage in terms of the experiment design:

  1. Compare a list of proteins versus the rest of the interactome (background).
  2. Compare two lists of proteins, e.g. two lists of transcipts with different pattern of expression.

Thus, SNOW has the possibility of performing the following objectives:

  • Find modules of genes/proteins with a structural component within lists of pre-selected proteins-genes and evaluate its topological parameters.
  • Evaluate the importance of a set of proteins/genes in the human interactome identifying hubs, central proteins and proteins in highly interconnected areas as well as evaluating the role of the list as a unit of action within the human interactome in terms of centrality, connectivity and clustering coefficient.
  • Load user's interaction data and find modules of proteins/genes defined by this data within pre-selected lists. Find the role of these lists in your interactomic data by evaluating its topological parameters.
  • Compare the role of two lists of proteins/genes in the human interactome.
  • Get the minimal connected network of two lists of proteins/genes and compare their topological parameters.

In this tutorial we will show the different usages of SNOW. Furthermore, there are several examples.

Network parameters evaluated

  • Connections (connections degree) - the number of edges, interaction events, for a node.
  • Betweenness - A measure of centrality. Calculated as the number of shortest paths that pass through a node divided the total of shortest path in the network.
  • Clustering coefficient - A measure of how interconnected the neighbours of that node are. Proportion of links between the vertices within its neighbourhood divided by the number of links that could possibly exist between them.
  • Component - a group of nodes connected among them.
  • Bicomponent - a group of nodes connected to another group of nodes by a single edge.

Databases and interactomes

We use human ppi datasets downloaded from the five main public databases:

  • IntAct (2011-01-19 version)
  • Biogrid ( version 3.1.72)
  • MINT (2011-01-19 version)

Entries in databases were mapped to UniProtKB and ensembl genes.

We used this collection of ppi data to generate two different types of interactomes for both transcripts and genes:

  • a non-filtered interactome - all available ppis.
  • and a filtered interactome - constraint in the methodologies. To build the filtered interactome the six top categories of experimental methods described in the Molecular Interaction (MI) Ontology. Every ppi in each of the datasets was annotated with these categories. Ppis verified by at least two of these methods were introduced in the filtered interactome.


  • Hsa - Homo sapiens
  • Sce - Saccharomyces cerevisiae
  • Ath - Arabidopsis thaliana
  • Bta - Bos taurus
  • Dme - Drosophila melanogaster
  • Eco - Escherichia coli (strain K12)
  • Mmu - Mus musculus


Instead of the usual Fisher’s or hypergeometric (or similar) tests, SNOW uses a Kolmogorov–Smirnov test to compare the distributions of the topological values of the proteins/genes to the background distribution of topological parameters (the interactome, in the first evaluation, or random MCN, in the second evaluation).


  1. Minguez P, Götz S, Montaner D, Al-Shahrour F, Dopazo J. SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. Nucleic Acids Res 2009 Jul;37(Web Server issue):W109-114.