SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks

Abstract

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

“…When we are interested in the edges that connect signaling pathways as proxies of crosstalk, a highly pertinent question to ask is whether the observed number of connecting edges is more or less than what would be expected by chance, given the connectivity structure of each network. The recently published version of SignaLink (70), which identifies signaling crosstalk and is similar to our work in that it is integrative and offers context-specificity, differs from our method in this important aspect: It is not a statistical framework; it does not perform any hypothesis testing but, rather, simply chronicles the number of connecting edges, and hence does not provide a sense of whether the number of edges between a pair of pathways is statistically meaningful within the underlying network of interactions. With MuXTalk, we generate interaction type-specific ensembles of randomized networks that act as null models and provide a standardized backdrop against which the number of edges can be compared, ensuring that the statistically over-represented multilinks are not simply byproducts of implicit and systematic data biases (71, 72).…”

Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment

“…TieDIE 70 was applied to find functional interactions between significantly associated TIME TFs and HNSC TIME drivers. The prior knowledge network (PKN) for TieDIE was assembled from 542,397 protein-protein 16 , 12,730 phosphorylation 71 , 15,104 genetic 57 and 34,877 signalling interactions 72 across 18,053 human genes. Fourteen TIME drivers–TIME TFs functional networks were rebuilt in each HNSC subtype and TIME feature, seven of which had an influence score significantly higher (p-value <0.…”

MAVEN: Compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny

“…Three inputs are taken; known or hypothesised targets which can be predicted from the compound’s chemical structure with PIDGINv4 4 or defined a priori (optional) (Figure 1A); a signed and directed (i.e., A activates/inhibits B) prior knowledge network (Figure 1B) for causal reasoning; and compound-induced gene expression data in the form of a summary statistic such as t-values or log2-fold changes (Figure 1C). A signed and directed prior knowledge network on causal protein-protein interactions is required to infer causality and function (activation or inhibition), and can be obtained from open source databases e.g., Omnipath 14 (provided), SignaLink 22 or SIGNOR 23 . Gene expression data in the form of differential expression signatures (i.e., Z-score, Log2FC, t-statistic) can be from any platform, e.g., microarray, RNA-Seq, and publicly available gene expression data is available for many perturbations in databases such as GEO (https://www.ncbi.nlm.nih.gov/geo/) 24 (provided for the compound lapatinib) and LINCS L1000 (https://clue.io/releases/data-dashboard-Level5) 2 .…”