Largest resource of human protein-protein interactions can help interpret genomic data

Human Interactome network visualized by Cytoscape 2.5. (Photo credit: Wikipedia 

An international research team has developed the largest database of protein-to-protein interaction networks, a resource that can illuminate how numerous disease-associated genes contribute to disease development and progression. Led by investigators at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard, the team's report on its development of the network called InWeb_InBioMap (InWeb_IM) is receiving advance online publication in Nature Methods.  

A scored human protein–protein interaction network to catalyze genomic interpretation

The database is at Intomics

EnrichNet: network-based gene set enrichment analysis.

Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized.

RESULTS:

To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins.

AVAILABILITY:

EnrichNet is freely available at http://www.enrichnet.org

The new age of proteomics: An integrative vision of the cellular world

"The enormous complexity of biological processes requires the use of high­performance technologies —also known as '­omics'—, that are capable of carrying out complete integrated analyses of the thousands of molecules that cells are made up of, and of studying their role in illnesses. In the post-genomic age we find ourselves in, the comprehensive study of cellular proteins —prote-omics— acquires a new dimension, as proteins are the molecular executors of genes and, therefore, the most important pieces of the puzzle if we wish to understand more completely how cells work."

The Core Diseasome. [Mol Biosyst. 2012]

Large amounts of protein-protein interaction (PPI) data are available. The human PPI network currently contains over 56 000 interactions between 11 100 proteins. It has been demonstrated that the structure of this network is not random and that the same wiring patterns in it underlie the same biological processes and diseases. In this paper, we ask if there exists a subnetwork of the human PPI network such that its topology is the key to disease formation and hence should be the primary object of therapeutic intervention. We demonstrate that such a subnetwork exists and can be obtained purely computationally. In particular, by successively pruning the entire human PPI network, we are left with a "core" subnetwork that is not only topologically and functionally homogeneous, but is also enriched in disease genes, drug targets, and it contains genes that are known to drive disease formation. We call this subnetwork the Core Diseasome. Furthermore, we show that the topology of the Core Diseasome is unique in the human PPI network suggesting that it may be the wiring of this network that governs the mutagenesis that leads to disease. Explaining the mechanisms behind this phenomenon and exploiting them remains a challenge.