The DifferentialNet database of differential protein–protein interactions in human tissues

Abstract

DifferentialNet is a novel database that provides users with differential interactome analysis of human tissues (http://netbio.bgu.ac.il/diffnet/). Users query DifferentialNet by protein, and retrieve its differential protein–protein interactions (PPIs) per tissue via an interactive graphical interface. To compute differential PPIs, we integrated available data of experimentally detected PPIs with RNA-sequencing profiles of tens of human tissues gathered by the Genotype-Tissue Expression consortium (GTEx) and by the Human Protein Atlas (HPA). We associated each PPI with a score that reflects whether its corresponding genes were expressed similarly across tissues, or were up- or down-regulated in the selected tissue. By this, users can identify tissue-specific interactions, filter out PPIs that are relatively stable across tissues, and highlight PPIs that show relative changes across tissues. The differential PPIs can be used to identify tissue-specific processes and to decipher tissue-specific phenotypes. Moreover, they unravel processes that are tissue-wide yet tailored to the specific demands of each tissue.

Transcriptomics & copy number variation of radiotoxicity points to altered mitochondrial and DNA repair mechanisms

“…For prostate tissue, 3113 genes were predicted and 5954 genes for whole-blood tissue. Following association tests, significant genes (identified as p-value <0.05) were investigated further by constructing tissue-specific protein-protein interaction (PPI) networks between query (significant genes) and interacting genes using the DifferentialNet [20] database and NetworkAnalyst3.0 [21]. The network was filtered on betweenness centrality of 4.0 in order to reduce isolated neighboring nodes (each gene is a node).…”

Differential network analysis of human tissue interactomes highlights tissue-selective processes and genetic disorder genes

“…Interactions Computed as the difference between the summed expression levels of the interacting nodes in the tissue and their median summed expression across tissues (see methods). Developed by (Basha, Shpringer, et al, 2017).…”

“…(2) = + 5. The differential interaction method, diff_i: In the interactome of tissue , an interaction between genes and was assigned a weight, denoted ′ , that was designed to reflect the probability for interaction given and expression levels (Basha, Shpringer, et al, 2017). This was approximated by * , namely the sum of their log2 normalized counts values (equation 3).…”

“…DN methods were applied to multiple types of molecular network models, including genetic interaction networks (Bandyopadhyay et al, 2010), co-expression networks (Ma et al, 2014;Pierson et al, 2015), PPI networks (Basha, Barshir, et al, 2017), and regulatory networks (Marbach et al, 2016). Computationally, DN methods focused on network nodes (e.g., (Sonawane et al, 2017), (Basha, Barshir, et al, 2017)), network interactions (e.g., (Basha, Shpringer, et al, 2017)), or network features such as node connectivity (e.g., (Goenawan et al, 2016)). DN methods proved useful for prediction of cancer genes (Islam et al, 2013;Warsow et al, 2013), drug targets (Zickenrott et al, 2016), and plants' stress response genes (Ma et al, 2014).…”

“…were implemented as open tools (e.g., (Gill et al, 2010;Landeghem et al, 2016;Gambardella et al, 2013;Ha et al, 2014)). In particular, we developed a DN method for tissue interactomes and made it available through the DifferentialNet database (Basha, Shpringer, et al, 2017). Specifically, each PPI within a tissue interactome was associated with a score, reflecting the up-or down-regulation of the interacting proteins relative to other tissues.…”