Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single‐cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter‐ and intracellular signaling, as well as transcriptional and post‐transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath ’s web service ( https://omnipathdb.org/ ), a Cytoscape plug‐in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra‐ and intercellular processes for data analysis, as we demonstrate in applications studying SARS‐CoV‐2 infection and ulcerative colitis.

Background: Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. Description: We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org. Conclusions: With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.

The toxicogenomics field aims to understand and predict toxicity by using ‘omics’ data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and ‘omics’ data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.