Studying Networks in the Omics World (SNOW)

Category Cross-Omics>Pathway Analysis/Tools

Abstract Studying Networks in the Omics World (SNOW) is a unique tool specifically designed to offer visualization and analysis of protein- protein interacting (PPI) networks, including their statistical analysis.

SNOW takes a list of proteins (or genes) and maps them onto an interactome of reference. This interactome can be the human interactome (two versions are available) or any other user-defined interactome.

Once the list is mapped, SNOW calculates several relevant network parameters for the proteins in the context of the interactome and the minimum connected network (MCN) defined by the proteins.

The corresponding tests are performed to assess the significance of the parameters calculated. Alternatively two lists of proteins can be compared by testing for significant deviations in their respective parameters’ distributions.

SNOW Databases --

Human PPI datasets were downloaded from five (5) main public databases:

1) The Human Protein Reference Database (HPRD);

2) IntAct (an open source resource for molecular interaction data);

3) the Biomolecular Interaction Network Database (BIND);

4) the Database of Interacting Proteins (DIP); and

5) the Molecular INTeraction database (MINT).

The manufacturer used this collection of PPI data to generate two (2) 'scaffold interactomes': a non-filtered scaffold interactome, which includes all the available PPIs; and a filtered, more confident scaffold interactome.

The six (6) top categories of experimental methods described in the Molecular Interaction (MI) Ontology 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.

SNOW Operational Input --

SNOW inputs a collection of protein (or gene) identifiers in plain text. Most of the standard protein and gene identifiers are accepted given that the tool uses the database of identifiers of Babelomics (see G6G Abstract Number 20274).

Alternatively, a user-defined interactome can be provided 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 Simple Interaction Format (SIF) format, used by Cytoscape (see G6G Abstract Number 20092).

Calculation of network parameters and statistics --

SNOW tests four (4) topological parameters:

1) Node connections degree, which was computed as the number of edges (interaction events) for a node;

2) Betweenness, which depends on the number of shortest pathways passing through a given node;

3) Clustering coefficient, which measures the connectivity of the neighborhood; and

4) Number of components of the network.

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, signaling networks tend to have high connectivity and low clustering coefficient while metabolic networks have higher clustering coefficients.

The three (3) 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 network, which can give info on the network properties.

Consequently, the comparison of two (2) networks by contrasting how different they are in terms of their characteristic parameters is straightforward by means of a Kolmogorov-Smirnov test.

The potential biological relevance 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.

By default the network is compared to the interactome of reference and to a network of random composition.

However, the direct comparison of the two networks is also possible. In addition, the same analysis can be performed by adding one (or two or even three) intermediate nodes (proteins originally Not included in the list that can actually 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.

SNOW Output --

SNOW outputs the average parameter values, their significance and box-plots representing the comparison of their actual distributions in the network studied to the complete interactome and to a random network of the same size.

SNOW outputs the number of components, bicomponents and articulation points. It also provides exhaustive info on these parameters as well as functional info of the proteins in the list.

Moreover, info 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 (see below...).

SNOW includes twelve (12) examples in which different sub-networks along with their corresponding parameters can be visualized.

SNOW 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 interactively changed.

Gene names can be displayed and connecting proteins (intermediate nodes) can be included or excluded from the graph.

System Requirements



Manufacturer Web Site SNOW

Price Contact manufacturer.

G6G Abstract Number 20499

G6G Manufacturer Number 102153